Dissertationen: „Multiagent decision“

1

Burkov, Andriy. „Leveraging Repeated Games for Solving Complex Multiagent Decision Problems“. Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28028/28028.pdf.

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Prendre de bonnes décisions dans des environnements multiagents est une tâche difficile dans la mesure où la présence de plusieurs décideurs implique des conflits d'intérêts, un manque de coordination, et une multiplicité de décisions possibles. Si de plus, les décideurs interagissent successivement à travers le temps, ils doivent non seulement décider ce qu'il faut faire actuellement, mais aussi comment leurs décisions actuelles peuvent affecter le comportement des autres dans le futur. La théorie des jeux est un outil mathématique qui vise à modéliser ce type d'interactions via des jeux stratégiques à plusieurs joueurs. Des lors, les problèmes de décision multiagent sont souvent étudiés en utilisant la théorie des jeux. Dans ce contexte, et si on se restreint aux jeux dynamiques, les problèmes de décision multiagent complexes peuvent être approchés de façon algorithmique. La contribution de cette thèse est triple. Premièrement, elle contribue à un cadre algorithmique pour la planification distribuée dans les jeux dynamiques non-coopératifs. La multiplicité des plans possibles est à l'origine de graves complications pour toute approche de planification. Nous proposons une nouvelle approche basée sur la notion d'apprentissage dans les jeux répétés. Une telle approche permet de surmonter lesdites complications par le biais de la communication entre les joueurs. Nous proposons ensuite un algorithme d'apprentissage pour les jeux répétés en ``self-play''. Notre algorithme permet aux joueurs de converger, dans les jeux répétés initialement inconnus, vers un comportement conjoint optimal dans un certain sens bien défini, et ce, sans aucune communication entre les joueurs. Finalement, nous proposons une famille d'algorithmes de résolution approximative des jeux dynamiques et d'extraction des stratégies des joueurs. Dans ce contexte, nous proposons tout d'abord une méthode pour calculer un sous-ensemble non vide des équilibres approximatifs parfaits en sous-jeu dans les jeux répétés. Nous montrons ensuite comment nous pouvons étendre cette méthode pour approximer tous les équilibres parfaits en sous-jeu dans les jeux répétés, et aussi résoudre des jeux dynamiques plus complexes.
Making good decisions in multiagent environments is a hard problem in the sense that the presence of several decision makers implies conflicts of interests, a lack of coordination, and a multiplicity of possible decisions. If, then, the same decision makers interact continuously through time, they have to decide not only what to do in the present, but also how their present decisions may affect the behavior of the others in the future. Game theory is a mathematical tool that aims to model such interactions as strategic games of multiple players. Therefore, multiagent decision problems are often studied using game theory. In this context, and being restricted to dynamic games, complex multiagent decision problems can be algorithmically approached. The contribution of this thesis is three-fold. First, this thesis contributes an algorithmic framework for distributed planning in non-cooperative dynamic games. The multiplicity of possible plans is a matter of serious complications for any planning approach. We propose a novel approach based on the concept of learning in repeated games. Our approach permits overcoming the aforementioned complications by means of communication between players. We then propose a learning algorithm for repeated game self-play. Our algorithm allows players to converge, in an initially unknown repeated game, to a joint behavior optimal in a certain, well-defined sense, without communication between players. Finally, we propose a family of algorithms for approximately solving dynamic games, and for extracting equilibrium strategy profiles. In this context, we first propose a method to compute a nonempty subset of approximate subgame-perfect equilibria in repeated games. We then demonstrate how to extend this method for approximating all subgame-perfect equilibria in repeated games, and also for solving more complex dynamic games.

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2

Gasparini, Luca. „Severity sensitive norm analysis and decision making“. Thesis, University of Aberdeen, 2017. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=231873.

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Normative systems have been proposed as a useful abstraction to represent ideals of behaviour for autonomous agents in a social context. They specify constraints that agents ought to follow, but may sometimes be violated. Norms can increase the predictability of a system and make undesired situations less likely. When designing normative systems, it is important to anticipate the effects of possible violations and understand how robust these systems are to violations. Previous research on robustness analysis of normative systems builds upon simplistic norm formalisms, lacking support for the speciﬁcation of complex norms that are often found in real world scenarios. Furthermore, existing approaches do not consider the fact that compliance with different norms may be more or less important in preserving some desirable properties of a system; that is, norm violations may vary in severity. In this thesis we propose models and algorithms to represent and reason about complex norms, where their violation may vary in severity. We build upon existing preference-based deontic logics and propose mechanisms to rank the possible states of a system according to what norms they violate, and their severity. Further, we propose mechanisms to analyse the properties of the system under different compliance assumptions, taking into account the severity of norm violations. Our norm formalism supports the speciﬁcation of norms that regulate temporally extended behaviour and those that regulate situations where other norms have been violated. We then focus on algorithms that allow coalitions of agents to coordinate their actions in order to minimise the risk of severe violations. We propose offline algorithms and heuristics for pre-mission planning in stochastic scenarios where there is uncertainty about the current state of the system. We then develop online algorithms that allow agents to maintain a certain degree of coordination and to use communication to improve their performance.

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3

Sosnowski, Scott T. „Approximate Action Selection For Large, Coordinating, Multiagent Systems“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459468867.

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4

Stamatopoulou, Anastasia. „AGGREGATION IN MULTIAGENT AND MULTICRITERIA DECISION MODELS: INTERACTION, DYNAMICS, AND MAXIMUM ENTROPY WEIGHTS IN THE FRAMEWORK OF CHOQUET INTEGRATION“. Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/311987.

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In the context of MCDM, the Choquet integral constitutes an interesting aggregation model which generalizes both the classical and the ordered weighted means. In the Choquet integration framework, an additive capacity generates a classical weighted mean, whereas a symmetric (non-additive) capacity generates an ordered weighted mean. Moreover, a general (non-additive) Choquet capacity induces a natural weighted mean, the Shapley mean, whose weights correspond to the so-called Shapley power indices. In the first part of the thesis, we examine a negotiation model which combines the Choquet integration framework with the classical Morris H. DeGroot 1974 model of consensus linear dynamics, in interactive multicriteria and multiagents networks. We consider a set $N={1,ldots,n }$ of interacting criteria (or agents) whose single evaluations (or individual opinions) are expressed in some domain $mathbb{D}subseteq mathbb{R}$. The interaction among the criteria (or agents) is expressed by a symmetric interaction matrix with null diagonal and off-diagonal coefficients in the open unit interval. The interaction network structure is thus that of a complete graph with edge values in $(0,1)$. In the Choquet integration framework, the interacting network structure is the basis for the construction of a capacity $mu$, whose Shapley indices are proportional to the average degree of interaction between criterion (or agent) $i$ and the remaining criteria (or agents). In relation with this interactive multicriteria (or multiagent) network model, we discuss three types of linear consensus dynamics, each of which represents a progressive aggregation process towards a consensual evaluation (or opinion) of the single criteria (or agents), corresponding to some form of mean of the original evaluations (or opinions). In the first type, the progressive aggregation converges simply to the plain mean of the original evaluations (or opinions) of the single criteria (or agents), while the second type converges to the Shapley mean of the original evaluations (or opinions). The third type, instead, converges to an emphasized form of Shapley mean, which we call superShapley mean. The interesting relation between Shapley and superShapley aggregation is investigated. In the second part of the thesis, we focus on entropy constrained optimization in the context of ordered weighted means, both in the classical Shannon entropy case, and in the more general Tsallis entropy case. The maximum entropy method is based on the solution of a nonlinear constrained optimization problem in which the OWA weights are obtained by maximizing the entropy, given a specified degree of orness. In the Shannon entropy case, we begin by reviewing the analytic solution of the maximum entropy method proposed by Filev and Yager in 1995, and later by Fuller and Majlender in 2001, and we consider the maximum entropy method in the binomial decomposition framework. Then, we present the optimization of the parametric Tsallis entropy function associated with Ordered Weighted Averaging. We examine the meaning of the entropic parameter $gamma$ in the context of OWA functions and how it affects the behavior of the associated entropy function. We introduce the nonlinear constrained optimization problem of Tsallis entropy for parameter values $gamma in (0,1)$ and we obtain the solution for the optimal weights in terms of the two Lagrange multipliers. Both in Shannon and Tsallis entropy cases for parameter $gamma in (0,1)$, the optimal weights for orness values in the open unit interval are positive (except for the extreme orness values $0,1$) and monotonic (increasing or decreasing) over the whole orness range $Omega in[0,1]$.

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5

Stamatopoulou, Anastasia. „AGGREGATION IN MULTIAGENT AND MULTICRITERIA DECISION MODELS: INTERACTION, DYNAMICS, AND MAXIMUM ENTROPY WEIGHTS IN THE FRAMEWORK OF CHOQUET INTEGRATION“. Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/311987.

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In the context of MCDM, the Choquet integral constitutes an interesting aggregation model which generalizes both the classical and the ordered weighted means. In the Choquet integration framework, an additive capacity generates a classical weighted mean, whereas a symmetric (non-additive) capacity generates an ordered weighted mean. Moreover, a general (non-additive) Choquet capacity induces a natural weighted mean, the Shapley mean, whose weights correspond to the so-called Shapley power indices. In the first part of the thesis, we examine a negotiation model which combines the Choquet integration framework with the classical Morris H. DeGroot 1974 model of consensus linear dynamics, in interactive multicriteria and multiagents networks. We consider a set $N={1,ldots,n }$ of interacting criteria (or agents) whose single evaluations (or individual opinions) are expressed in some domain $mathbb{D}subseteq mathbb{R}$. The interaction among the criteria (or agents) is expressed by a symmetric interaction matrix with null diagonal and off-diagonal coefficients in the open unit interval. The interaction network structure is thus that of a complete graph with edge values in $(0,1)$. In the Choquet integration framework, the interacting network structure is the basis for the construction of a capacity $mu$, whose Shapley indices are proportional to the average degree of interaction between criterion (or agent) $i$ and the remaining criteria (or agents). In relation with this interactive multicriteria (or multiagent) network model, we discuss three types of linear consensus dynamics, each of which represents a progressive aggregation process towards a consensual evaluation (or opinion) of the single criteria (or agents), corresponding to some form of mean of the original evaluations (or opinions). In the first type, the progressive aggregation converges simply to the plain mean of the original evaluations (or opinions) of the single criteria (or agents), while the second type converges to the Shapley mean of the original evaluations (or opinions). The third type, instead, converges to an emphasized form of Shapley mean, which we call superShapley mean. The interesting relation between Shapley and superShapley aggregation is investigated. In the second part of the thesis, we focus on entropy constrained optimization in the context of ordered weighted means, both in the classical Shannon entropy case, and in the more general Tsallis entropy case. The maximum entropy method is based on the solution of a nonlinear constrained optimization problem in which the OWA weights are obtained by maximizing the entropy, given a specified degree of orness. In the Shannon entropy case, we begin by reviewing the analytic solution of the maximum entropy method proposed by Filev and Yager in 1995, and later by Fuller and Majlender in 2001, and we consider the maximum entropy method in the binomial decomposition framework. Then, we present the optimization of the parametric Tsallis entropy function associated with Ordered Weighted Averaging. We examine the meaning of the entropic parameter $gamma$ in the context of OWA functions and how it affects the behavior of the associated entropy function. We introduce the nonlinear constrained optimization problem of Tsallis entropy for parameter values $gamma in (0,1)$ and we obtain the solution for the optimal weights in terms of the two Lagrange multipliers. Both in Shannon and Tsallis entropy cases for parameter $gamma in (0,1)$, the optimal weights for orness values in the open unit interval are positive (except for the extreme orness values $0,1$) and monotonic (increasing or decreasing) over the whole orness range $Omega in[0,1]$.

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6

Warden, Tobias [Verfasser], Otthein [Akademischer Betreuer] Herzog, Otthein [Gutachter] Herzog und Winfried [Gutachter] Lamersdorf. „Interactive Multiagent Adaptation of Individual Classification Models for Decision Support / Tobias Warden ; Gutachter: Otthein Herzog, Winfried Lamersdorf ; Betreuer: Otthein Herzog“. Bremen : Staats- und Universitätsbibliothek Bremen, 2019. http://d-nb.info/1199003611/34.

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7

Barfuss, Wolfram. „Learning dynamics and decision paradigms in social-ecological dilemmas“. Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20127.

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Kollektives Handeln ist erforderlich um nachhaltige Entwicklungspfade in gekoppelten sozial-ökologischen Systemen zu erschließen, fernab von gefährlichen Kippelementen. Ohne anderen Modellierungsprinzipien ihren Nutzen abzuerkennen, schlägt diese Dissertation die Agent-Umwelt Schnittstelle als die mathematische Grundlage für das Modellieren sozial-ökologischer Systeme vor. Zuerst erweitert diese Arbeit eine Methode aus der Literatur der statistischen Physik über Lerndynamiken, um einen deterministischen Grenzübergang von etablierten Verstärkungslernalgorithmen aus der Forschung zu künstlicher Intelligenz herzuleiten. Die resultierenden Lerndynamiken zeigen eine große Bandbreite verschiedener dynamischer Regime wie z.B. Fixpunkte, Grenzzyklen oder deterministisches Chaos. Zweitens werden die hergeleiteten Lerngleichungen auf eine neu eingeführte Umwelt, das Ökologisches Öffentliches Gut, angewendet,. Sie modelliert ein gekoppeltes sozial-ökologisches Dilemma und erweitert damit etablierte soziale Dilemmaspiele um ein ökologisches Kippelement. Bekannte theoretische und empirische Ergebnisse werden reproduziert und neuartige, qualitativ verschiedene Parameterregime aufgezeigt, darunter eines, in dem diese belohnungsoptimierenden Lern-Agenten es vorziehen, gemeinsam unter einem Kollaps der Umwelt zu leiden, als in einer florierenden Umwelt zu kooperieren. Drittens stellt diese Arbeit das Optimierungsparadigma der Lern-Agenten in Frage. Die drei Entscheidungsparadimen ökonomischen Optimierung, Nachhaltigkeit und Sicherheit werden systematisch miteinander verglichen, während sie auf das Management eines umweltlichen Kippelements angewendet werden. Es wird gezeigt, dass kein Paradigma garantiert, Anforderungen anderer Paradigmen zu erfüllen, sowie dass das Fehlen eines Meisterparadigmas von besonderer Bedeutung für das Klimasystem ist, da dieses sich am Rand zwischen Parameterbereichen befinden kann, wo ökonomische Optimierung weder nachhaltig noch sicher wird.
Collective action is required to enter sustainable development pathways in coupled social-ecological systems, safely away from dangerous tipping elements. Without denying the usefulness of other model design principles, this thesis proposes the agent-environment interface as the mathematical foundation for the design of social-ecological system models. First, this work refines techniques from the statistical physics literature on learning dynamics to derive a deterministic limit of established reinforcement learning algorithms from artificial intelligence research. Illustrations of the resulting learning dynamics reveal a wide range of different dynamical regimes, such as fixed points, periodic orbits and deterministic chaos. Second, the derived multi-state learning equations are applied to a newly introduced environment, the Ecological Public Good. It models a coupled social-ecological dilemma, extending established repeated social dilemma games by an ecological tipping element. Known theoretical and empirical results are reproduced and novel qualitatively different parameter regimes are discovered, including one in which these reward-optimizing agents prefer to collectively suffer in environmental collapse rather than cooperating in a prosperous environment. Third, this thesis challenges the reward optimizing paradigm of the learning equations. It presents a novel formal comparison of the three decision paradigms of economic optimization, sustainability and safety for the governance of an environmental tipping element. It is shown that no paradigm guarantees fulfilling requirements imposed by another paradigm. Further, the absence of a master paradigm is shown to be of special relevance for governing the climate system, since the latter may reside at the edge between parameter regimes where economic welfare optimization becomes neither sustainable nor safe.

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Serramia, Amoros Marc. „Value-aligned norm selection“. Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/672634.

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Norms have been widely enacted in both human and agent societies to regulate the actions that individuals can perform. However, although legislators may have ethics in mind when establishing norms, moral values are seldom explicitly considered. This thesis advances the state of the art in normative multi-agent systems by providing quantitative and qualitative methods for a decision maker to select the norms to enact within a society that best align with the moral values of such society. We call the problem of selecting these norms, the value-aligned norm selection. The quantitative approach to align norms and values is grounded on the ethics literature. Specifically, from the study of the relations between norms, actions and values in the literature, we formally define how actions and values relate, through the so-called value judgement functions, and how norms and values relate, through the so-called norm promotion functions. We show that both functions provide the means to compute value alignment for a set of norms, and also that our norm selection problem can be cast as an optimisation problem: finding the set of norms that maximises value alignment. Furthermore, we provide a binary integer program (BIP) encoding to solve the value-aligned norm selection problem with off-the-shelf solvers. While utilitarian approaches are commonplace in multi-criteria decision making, utilities may not always be available or easy to specify. In the case of value-aligned norm selection, assessing numerically how a norm relates to a value may not be easy for a decision maker. In more general terms, decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on qualitative preferences regarding some criteria. In fact, this constitutes a family of problems, which we formalise as dominant set selection problems (DSSP). We propose two approaches to solve the DSSP depending on how elements relate to the criteria. Both approaches are based on transforming the criteria preferences to preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over criteria to preferences over the objects themselves; and (ii) lifting these preferences to preferences over all possible sets of objects. Since the value-aligned norm selection problem is a particular instance of the DSSP, we can readily adapt the proposed qualitative approaches to perform value-aligned norm selection. Our first qualitative approach supposes binary relations between elements and criteria. In the case of value-aligned norm selection, norms either promote or do not promote values. This approach relies on combining lex-cel (an existing method in the literature to ground preferences over criteria to preferences over elements) with our novel anti-lex-cel (a function that lifts preferences over elements to preferences over sets of these elements), which we formally (and thoroughly) study. Furthermore, we provide a BIP encoding for the DSSP to solve it with optimisation libraries. Building on the first approach, we consider labelled relations between elements and criteria. For example, in the case of value-aligned norm selection, norms can promote or demote values with different degrees, we can capture these degrees of promotion and demotion through labels. This calls for a new decision making framework, which we formally introduce. Within such framework, we introduce a new method to ground preferences over criteria to preferences over single elements considering the labelled element-criterion relations: multi-criteria lex-cel. The resolution of the value-aligned norm selection problem in this case relies on the combination of multi-criteria lex-cel and anti-lex-cel. Here, we also provide a BIP encoding to solve the DSSP. Furthermore, we formally establish that the contributions of this second approach generalise recent results in the social choice literature.
Les normes s’utilitzen àmpliament en societats tant d'humans com d'agents per a regular les accions dels seus individus. Tanmateix, tot i que els legisladors poden estar considerant aspectes ètics de forma intrínseca en definir normes, aquests aspectes no són usualment considerats de forma explícita. Aquesta tesi avança l'estat de l'art en sistemes multiagent normatius formalitzant mètodes quantitatius i qualitatius per seleccionar normes basant-se en els valors morals i les preferències sobre aquests valors. Anomenem aquest procés: selecció de normes alineades als valors. L’aproximació quantitativa a la selecció de normes alineades als valors està basada en la literatura d'ètica. Arran de l'estudi de les relacions entre normes, accions i valors que es fa a la literatura, proposem una definició formal de les relacions entre accions i valors a través de les funcions de judici, i de les relacions entre normes i valors a través de les funcions de promoció. Utilitzem aquestes funcions per calcular l’alineament d’un conjunt de normes amb els valors. D'aquesta manera, la selecció de normes consisteix a trobar el conjunt de normes que maximitzin l’alineament amb els valors. Tot i que les resolucions basades en utilitats són comunes en la presa de decisions, especificar utilitats pot ser una tasca difícil o impossible. Per exemple, no és fàcil avaluar numèricament l'impacte d'una norma sobre un valor. En termes més generals, la selecció d’alguns elements d'un conjunt de candidats, sol estar guiada per criteris de decisió. Identifiquem aquesta família de problemes que anomenem problemes de selecció del conjunt dominant. Proposem dues resolucions per a aquests problemes depenent de com s'especifiquen les relacions entre els elements i els criteris de decisió. Les dues resolucions transformen les preferències sobre criteris en preferències sobre conjunts d'elements. Ho fem en dos passos: (i) transformem les preferències sobre criteris en preferències sobre elements; i (ii) transformem les preferències sobre elements en preferències sobre conjunts d'aquests elements. La solució és el conjunt més preferit. Com que el problema de selecció de normes és una instància de la família de problemes de selecció del conjunt dominant, podem adaptar aquestes resolucions per a la selecció de normes.

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Vieira, Fábio Lopes. „UM SISTEMA MULTIAGENTE PARA APOIO AS DECISÕES NO PROCESSO DE LICITAÇÃO PÚBLICA“. Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/499.

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Made available in DSpace on 2016-08-17T14:53:23Z (GMT). No. of bitstreams: 1 dissertacao Fabio Lopes.pdf: 2519942 bytes, checksum: 52dd144296d75a73ab8348ec4c078f84 (MD5) Previous issue date: 2013-01-07
Public licitation is an administrative process which goal is to purchase goods or services to the sectors belonging to the public administration and follow the rules of law no. 8.666/93. In this process public officials need to take decisions such as choosing the type and modality of the licitation. Due to the complexity of the law governing the licitation process and the great possibilities of conducting the licitation process in different scenarios and also its dynamism in the face of constant changes in legislation, we developed a multi-agent system to optimize the decisions of those responsible for acquiring goods and services for the public administration. A Multiagent System is a system composed of several agents that communicate and are collectively capable of achieving goals that they would not be able to meet separately. The complexity of these systems is approached through interactions between agents. We used the exchange messages architecture, where agents communicate directly with each other through asynchronous messages using a chat protocol, which sets the rules and enforces the formalism necessary for messages to be sent and understood by the agents. To specify the system we adopted MADAE-Pro, a process which guides the development of multi-agent systems through the phases of specification, design and implementation.
A licitação pública é um processo administrativo cujo objetivo é a compra de bens ou serviços para os órgãos pertencentes à Administração Pública e segue as normas da lei nº. 8.666/93. Nesse processo há necessidade de que os agentes públicos envolvidos tomem decisões como a escolha do tipo e da modalidade da licitação. Devido a complexidade da Lei que regula o processo de licitação e às inúmeras possibilidades de condução do processo licitatório em diversos cenários e também a seu dinamismo, diante das constantes alterações na legislação; foi desenvolvido um sistema multiagente para o processo de licitação pública visando otimizar a tomada de decisões dos responsáveis pela aquisição dos bens e serviços na Administração Pública. Um Sistema Multiagente é um sistema composto por vários agentes que se comunicam e são coletivamente capazes de atingir objetivos que não seriam capazes de satisfazer separadamente. A complexidade destes sistemas é abordada através das interações entre os agentes, ou seja, cada agente pode executar, dentro de suas limitações, uma tarefa simples, mas a boa coordenação da execução dessas tarefas simples por cada agente torna o sistema capaz de executar tarefas de grande complexidade. Foi utilizada a arquitetura de troca de mensagem entre agentes, onde os agentes se comunicam diretamente uns com os outros, através de mensagens assíncronas, utilizando um protocolo de conversação, o qual dita as regras e impõe o formalismo necessário para que as mensagens sejam encaminhadas e compreendidas pelos agentes. Para fazer a especificação do sistema, adotou-se o MADAEPro, um processo que guia o desenvolvimento de um sistema multiagente nas fases de especificação, projeto e implementação.

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Sotnik, Garry. „SOSIEL: a Cognitive, Multi-Agent, and Knowledge-Based Platform for Modeling Boundedly-Rational Decision-Making“. PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4239.

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Decision-related activities, such as bottom-up and top-down policy development, analysis, and planning, stand to benefit from the development and application of computer-based models that are capable of representing spatiotemporal social human behavior in local contexts. This is especially the case with our efforts to understand and search for ways to mitigate the context-specific effects of climate change, in which case such models need to include interacting social and ecological components. The development and application of such models has been significantly hindered by the challenges in designing artificial agents whose behavior is grounded in both empirical evidence and theory and in testing the ability of artificial agents to represent the behavior of real-world decision-makers. This dissertation advances our ability to develop such models by overcoming these challenges through the creation of: (a) three new frameworks, (b) two new methods, and (c) two new open-source modeling tools. The three new frameworks include: (a) the SOSIEL framework, which provides a theoretically-grounded blueprint for the development of a new generation of cognitive, multi-agent, and knowledge-based models that consist of agents empowered with cognitive architectures; (b) a new framework for analyzing the bounded rationality of decision-makers, which offers insight into and facilitates the analysis of the relationship between a decision situation and a decision-maker's decision; and (c) a new framework for analyzing the doubly-bounded rationality (DBR) of artificial agents, which does the same for the relationship between a decision situation and an artificial agent's decision. The two new methods include: (a) the SOSIEL method for acquiring and operationalizing decision-making knowledge, which advances our ability to acquire, process, and represent decision-making knowledge for cognitive, multi-agent, and knowledge-based models; and (b) the DBR method for testing the ability of artificial agents to represent human decision-making. The two open-source modeling tools include: (a) the SOSIEL platform, which is a cognitive, multi-agent, and knowledge-based platform for simulating human decision-making; and (b) an application of the platform as the SOSIEL Human Extension (SHE) to an existing forest-climate change model, called LANDIS-II, allowing for the analysis of co-evolutionary human-forest-climate interactions. To provide a context for examples and also guidelines for knowledge acquisition, the dissertation includes a case study of social-ecological interactions in an area of the Ukrainian Carpathians where LANDIS-II with SHE are currently being applied. As a result, this dissertation advances science by: (a) providing a theoretical foundation for and demonstrating the implementation of a next generation of models that are cognitive, multi-agent, and knowledge-based; and (b) providing a new perspective for understanding, analyzing, and testing the ability of artificial agents to represent human decision-making that is rooted in psychology.

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Reddy, Prashant P. „Semi-Cooperative Learning in Smart Grid Agents“. Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/542.

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Striving to reduce the environmental impact of our growing energy demand creates tough new challenges in how we generate and use electricity. We need to develop Smart Grid systems in which distributed sustainable energy resources are fully integrated and energy consumption is efficient. Customers, i.e., consumers and distributed producers, require agent technology that automates much of their decision-making to become active participants in the Smart Grid. This thesis develops models and learning algorithms for such autonomous agents in an environment where customers operate in modern retail power markets and thus have a choice of intermediary brokers with whom they can contract to buy or sell power. In this setting, customers face a learning and multiscale decision-making problem – they must manage contracts with one or more brokers and simultaneously, on a finer timescale, manage their consumption or production levels under existing contracts. On a contextual scale, they can optimize their isolated selfinterest or consider their shared goals with other agents. We advance the idea that a Learning Utility Management Agent (LUMA), or a network of such agents, deployed on behalf of a Smart Grid customer can autonomously address that customer’s multiscale decision-making responsibilities. We study several relationships between a given LUMA and other agents in the environment. These relationships are semi-cooperative and the degree of expected cooperation can change dynamically with the evolving state of the world. We exploit the multiagent structure of the problem to control the degree of partial observability. Since a large portion of relevant hidden information is visible to the other agents in the environment, we develop methods for Negotiated Learning, whereby a LUMA can offer incentives to the other agents to obtain information that sufficiently reduces its own uncertainty while trading off the cost of offering those incentives. The thesis first introduces pricing algorithms for autonomous broker agents, time series forecasting models for long range simulation, and capacity optimization algorithms for multi-dwelling customers. We then introduce Negotiable Entity Selection Processes (NESP) as a formal representation where partial observability is negotiable amongst certain classes of agents. We then develop our ATTRACTIONBOUNDED- LEARNING algorithm, which leverages the variability of hidden information for efficient multiagent learning. We apply the algorithm to address the variable-rate tariff selection and capacity aggregate management problems faced by Smart Grid customers. We evaluate the work on real data using Power TAC, an agent-based Smart Grid simulation platform and substantiate the value of autonomous Learning Utility Management Agents in the Smart Grid.

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Salmon, John LaNay. „A methodology for quantitative and cooperative decision making of air mobility operational solutions“. Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49039.

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Many complex and interdependent systems engineering challenges involve more than one stakeholder or decision maker. These challenges, such as the definition and acquisition of future air mobility systems, are often found in situations where resources are finite, objectives are conflicting, constraints are restricting, and uncertainty in future outcomes prevail. Air mobility operational models which simulate fleet wide behavior effects over time, in various mission scenarios, and potentially over the entire design life-cycle, are always multi-dimensional, cover a large decision space, and require significant time to generate sufficient solutions to adequately describe the design space. This challenge is coupled with the fact that, in these highly integrated solutions or acquisitions, multiple stakeholders or decision makers are required to cooperate and reach agreement in selecting or defining the requirements for the design or solution and in its costly and lengthy implementation. However, since values, attitudes, and experiences are different for each decision maker, reaching consensus across the multiple criteria with different preferences and objectives is often a slow and highly convoluted process. In response to these common deficiencies and to provide quantitative analyses, this research investigates and proposes solutions to two challenges: 1) increase the speed at which operational solutions and associated requirements are generated and explored, and 2) systematize the group decision-making process, to both accelerate and improve decision making in these large operational problems requiring cooperation. The development of the Air Mobility Operations Design (AirMOD) model is proposed to address the first challenge by implementing and leveraging surrogate models of airlift capability across a wide scenario space. In addressing the second major challenge, the proposed Multi-Agent Consensus Reaching on the Objective Space (MACRO) methodology introduces a process to reduce the feasible decision space, by identifying regions of high probability of consensus reaching, using preference distributions, power relationships, and game-theoretic techniques. In a case study, the MACRO methodology is demonstrated on a large air mobility solution space generated by AirMOD to illustrate plausibility of the overall approach. AirMOD and MACRO offer considerable advantages over current methods to better define the operational design space and improve group decision-making processes requiring cooperation, respectively.

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Calliess, Jan-Peter. „Conservative decision-making and inference in uncertain dynamical systems“. Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:b7206c3a-8d76-4454-a258-ea1e5bd1c63e.

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The demand for automated decision making, learning and inference in uncertain, risk sensitive and dynamically changing situations presents a challenge: to design computational approaches that promise to be widely deployable and flexible to adapt on the one hand, while offering reliable guarantees on safety on the other. The tension between these desiderata has created a gap that, in spite of intensive research and contributions made from a wide range of communities, remains to be filled. This represents an intriguing challenge that provided motivation for much of the work presented in this thesis. With these desiderata in mind, this thesis makes a number of contributions towards the development of algorithms for automated decision-making and inference under uncertainty. To facilitate inference over unobserved effects of actions, we develop machine learning approaches that are suitable for the construction of models over dynamical laws that provide uncertainty bounds around their predictions. As an example application for conservative decision-making, we apply our learning and inference methods to control in uncertain dynamical systems. Owing to the uncertainty bounds, we can derive performance guarantees of the resulting learning-based controllers. Furthermore, our simulations demonstrate that the resulting decision-making algorithms are effective in learning and controlling under uncertain dynamics and can outperform alternative methods. Another set of contributions is made in multi-agent decision-making which we cast in the general framework of optimisation with interaction constraints. The constraints necessitate coordination, for which we develop several methods. As a particularly challenging application domain, our exposition focusses on collision avoidance. Here we consider coordination both in discrete-time and continuous-time dynamical systems. In the continuous-time case, inference is required to ensure that decisions are made that avoid collisions with adjustably high certainty even when computation is inevitably finite. In both discrete-time and finite-time settings, we introduce conservative decision-making. That is, even with finite computation, a coordination outcome is guaranteed to satisfy collision-avoidance constraints with adjustably high confidence relative to the current uncertain model. Our methods are illustrated in simulations in the context of collision avoidance in graphs, multi-commodity flow problems, distributed stochastic model-predictive control, as well as in collision-prediction and avoidance in stochastic differential systems. Finally, we provide an example of how to combine some of our different methods into a multi-agent predictive controller that coordinates learning agents with uncertain beliefs over their dynamics. Utilising the guarantees established for our learning algorithms, the resulting mechanism can provide collision avoidance guarantees relative to the a posteriori epistemic beliefs over the agents' dynamics.

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Kpakpo, Miguel. „Une approche de gestion de la maintenance de parcs éoliens centrée sur les systèmes multiagents“. Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR33/document.

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L’optimisation de la maintenance industrielle revêt différents aspects suivant les objectifs fixés par l’exploitant industriel. L’objectif le plus courant est la réduction des arrêts et des pannes. Le but est d’assurer une disponibilité élevée de l’équipement. Nous allons plus loin en nous posant la question de l’efficience des coûts de maintenance et de la rentabilité. La réponse donnée ici à cette question provient des résultats d’une fonction de coût associée à une plateforme de simulation basée sur les systèmes multiagents. Le choix du paradigme Agent est motivé par l’utilisation des SMA à d’autres fins de simulation et qu’ils garantissent une forme de souplesse quant à l’évolution du contexte métier. La thèse porte sur un modèle de systèmes multiagents destiné à améliorer la gestion des parcs éoliens à travers la définition d'un ensemble de critères financiers propres à l’exploitant éolien
Optimization & maintenance in the Industrial sector covers different aspects according to the objectives set by the industrial operator. Their common goal is to reduce downtime and failures. For the windfarm operators the goal is to ensure the wind farms high availibility. We went one step further by asking the question of the efficiency of maintenance costs and the profitability. The answer to this question comes from the results of a cost function associated to a simulation model based on multiagents systems. The choice of the multiagent paradigm is motivated by the use of MAS for other simulation purposes and the fact that they guarantee a kind of flexibility regarding the evolution in a moving business context. This Phd thesis focuses on a multi-agent systems model designed to improve the management of wind farms through the definition of a set of financial criteria specific to the wind farm operators

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Desquesnes, Guillaume Louis Florent. „Distribution de Processus Décisionnels Markoviens pour une gestion prédictive d’une ressource partagée : application aux voies navigables des Hauts-de-France dans le contexte incertain du changement climatique“. Thesis, Ecole nationale supérieure Mines-Télécom Lille Douai, 2018. http://www.theses.fr/2018MTLD0001/document.

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Les travaux de cette thèse visent à mettre en place une gestion prédictive sous incertitudes de la ressource en eau pour les réseaux de voies navigables. L'objectif est de proposer un plan de gestion de l'eau pour optimiser les conditions de navigation de l'ensemble du réseau supervisé sur un horizon spécifié. La solution attendue doit rendre le réseau résilient aux effets probables du changement climatique et aux évolutions du trafic fluvial. Dans un premier temps, une modélisation générique d'une ressource distribuée sur un réseau est proposée. Celle-ci, basée sur les processus décisionnels markoviens, prend en compte les nombreuses incertitudes affectant les réseaux considérés. L'objectif de cette modélisation est de couvrir l'ensemble des cas possibles, prévus ou non, afin d'avoir une gestion résiliente de ces réseaux. La seconde contribution consiste en une distribution du modèle sur plusieurs agents afin de permettre son passage à l'échelle. Ceci consiste en une répartition des capacités de contrôle du réseau entre les agents. Chaque agent ne possède ainsi qu'une connaissance locale du réseau supervisé. De ce fait, les agents ont besoin de se cordonner pour proposer une gestion efficace du réseau. Une résolution itérative avec échanges de plans temporaires de chaque agent est utilisée pour l'obtention de politiques de gestion locales à chaque agent. Finalement, des expérimentations ont été réalisées sur des réseaux réels de voies navigables françaises pour observer la qualité des solutions produites. Plusieurs scénarios climatiques différents ont été simulés pour tester la résilience des politiques produites
The work of this thesis aims to introduce and implement a predictive management under uncertainties of the water resource for inland waterway networks. The objective is to provide a water management plan to optimize the navigation conditions of the entire supervised network over a specified horizon. The expected solution must render the network resilient to probable effects of the climate change and changes in waterway traffic. Firstly, a generic modeling of a resource distributed on a network is proposed. This modeling, based on Markovian Decision Processes, takes into account the numerous uncertainties affecting considered networks. The objective of this modeling is to cover all possible cases, foreseen or not, in order to have a resilient management of those networks. The second contribution consists in a distribution of the model over several agents to facilitate the scaling. This consists of a repartition of the network's control capacities among the agents. Thus, each agent has only local knowledge of the supervised network. As a result, agents require coordination to provide an efficient management of the network. An iterative resolution, with exchanges of temporary plans from each agent, is used to obtain local management policies for each agent. Finally, experiments were carried out on realistic and real networks of the French waterways to observe the quality of the solutions produced. Several different climatic scenarios have been simulated to test the resilience of the produced policies

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Gonçalves, Luciano Vargas. „Uma arquitetura de Agentes BDI para auto-regulação de Trocas Sociais em Sistemas Multiagentes Abertos“. Universidade Catolica de Pelotas, 2009. http://tede.ucpel.edu.br:8080/jspui/handle/tede/105.

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The study and development of systems to control interactions in multiagent systems is an open problem in Artificial Intelligence. The system of social exchange values of Piaget is a social approach that allows for the foundations of the modeling of interactions between agents, where the interactions are seen as service exchanges between pairs of agents, with the evaluation of the realized or received services, thats is, the investments and profits in the exchange, and credits and debits to be charged or received, respectively, in future exchanges. This evaluation may be performed in different ways by the agents, considering that they may have different exchange personality traits. In an exchange process along the time, the different ways in the evaluation of profits and losses may cause disequilibrium in the exchange balances, where some agents may accumulate profits and others accumulate losses. To solve the exchange equilibrium problem, we use the Partially Observable Markov Decision Processes (POMDP) to help the agent decision of actions that can lead to the equilibrium of the social exchanges. Then, each agent has its own internal process to evaluate its current balance of the results of the exchange process between the other agents, observing its internal state, and with the observation of its partner s exchange behavior, it is able to deliberate on the best action it should perform in order to get the equilibrium of the exchanges. Considering an open multiagent system, it is necessary a mechanism to recognize the different personality traits, to build the POMDPs to manage the exchanges between the pairs of agents. This recognizing task is done by Hidden Markov Models (HMM), which, from models of known personality traits, can approximate the personality traits of the new partners, just by analyzing observations done on the agent behaviors in exchanges. The aim of this work is to develop an hybrid agent architecture for the self-regulation of social exchanges between personalitybased agents in a open multiagent system, based in the BDI (Beliefs, Desires, Intentions) architecture, where the agent plans are obtained from optimal policies of POMDPs, which model personality traits that are recognized by HMMs. To evaluate the proposed approach some simulations were done considering (known or new) different personality traits
O estudo e desenvolvimento de sistemas para o controle de interações em sistemas multiagentes é um tema em aberto dentro da Inteligência Artificial. O sistema de valores de trocas sociais de Piaget é uma abordagem social que possibilita fundamentar a modelagem de interações de agentes, onde as interações são vistas como trocas de serviços entre pares de agentes, com a valorização dos serviços realizados e recebidos, ou seja, investimentos e ganhos na troca realizada, e, também os créditos e débitos a serem cobrados ou recebidos, respectivamente, em trocas futuras. Esta avaliação pode ser realizada de maneira diferenciada pelos agentes envolvidos, considerando que estes apresentam traços de personalidade distintos. No decorrer de processo de trocas sociais a forma diferenciada de avaliar os ganhos e perdas nas interações pode causar desequilíbrio nos balanços de trocas dos agentes, onde alguns agentes acumulam ganhos e outros acumulam perdas. Para resolver a questão do equilíbrio das trocas, encontrou-se nos Processos de Decisão de Markov Parcialmente Observáveis (POMDP) uma metodologia capaz de auxiliar a tomada de decisões de cursos de ações na busca do equilíbrio interno dos agentes. Assim, cada agente conta com um mecanismo próprio para avaliar o seu estado interno, e, de posse das observações sobre o comportamento de troca dos parceiros, torna-se apto para deliberar sobre as melhores ações a seguir na busca do equilíbrio interno para o par de agentes. Com objetivo de operar em sistema multiagentes aberto, torna-se necessário um mecanismo para reconhecer os diferentes traços de personalidade, viabilizando o uso de POMDPs nestes ambientes. Esta tarefa de reconhecimento é desempenhada pelos Modelos de Estados Ocultos de Markov (HMM), que, a partir de modelos de traços de personalidade conhecidos, podem inferir os traços aproximados de novos parceiros de interações, através das observações sobre seus comportamentos nas trocas. O objetivo deste trabalho é desenvolver uma arquitetura de agentes híbrida para a auto-regulação de trocas sociais entre agentes baseados em traços de personalidade em sistemas multiagentes abertos. A arquitetura proposta é baseada na arquitetura BDI (Beliefs, Desires, Intentions), onde os planos dos agentes são obtidos através de políticas ótimas de POMDPs, que modelam traços de personalidade reconhecidos através de HMMs. Para avaliar a proposta, foram realizadas simulações envolvendo traços de personalidade conhecidos e novos traços

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Яцишин, М. М. „Комп'ютерне моделювання та прогнозування відкладення неорганічних речовин у стовбурі газових свердловин“. Thesis, Івано-Франківський національний технічний університет нафти і газу, 2011. http://elar.nung.edu.ua/handle/123456789/1942.

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Дисертація присвячується аналізу сучасного отану у розробці методів управління при підтримці прийнятих рішень у процесі прогнозування утворення відкладень неорганічних речовин, виділено основні параметри, які характеризують процес кристалізації солей у стовбурі свердловини. Розроблено математично-інформаційну модель прогнозування процесу утворення неорганічних відкладів по стовбуру свердловини. Введено підхід до опису процесу прогнозування утворення неорганічних відкладень по стовбуру свердловини за допомогою теорії категорій. Розроблено алгоритм інформаційної системи підтримки прийняття рішень при прогнозуванні неорганічних речовин по cтовбуру свердловини при видобутку газу.
The dissertation is devoted to analyzing the current state development methods of management in support decisions in the process of prognostication the formation inorganic deposits, basic parameters that characterize the process of the salts crystallization in the hole. The mathematical model of prognostication and information process formation of inorganic deposits on the mining hole is developed. Approach introduced to describe the process of prognostication the formation of inorganic deposits in the hole by the theory of categories. A algorithm information system for decision support in predicting inorganic substances in the mining hole in the extraction of gas.

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Shams, Parham. „Procedures based on Exchanges and new Relaxations of Envy-Freeness in Fair Division of Indivisible Goods“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS477.

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Cette thèse s'inscrit dans le contexte du Choix Social Computationnel. Il s'agit d'un domaine à l'intersection du Choix Social, de l'Informatique et de l'Intelligence Artificielle. Nous nous intéressons plus particulièrement au problème partage équitable de ressources indivisibles qui consiste à trouver une allocation équitable et efficace d'un ensemble d'objets à un ensemble d'agents. Tandis que la notion d'efficacité est communément traduite par l'exigence minimale de complétude (tous les objets doivent être alloués dans le but de ne gâcher aucun objet) ou la notion plus exigeante de Pareto-Optimalité (une allocation est dite Pareto-Optimale s'il n'existe pas une autre allocation telle que tous les agents sont au moins aussi contents et un agent est strictement plus content), plusieurs notions ont été proposées pour définir l'équité. Une des mesures d'équité la plus importante est l'absence d'envie. Une allocation est dite sans envie ou envy-free si aucun agent n'aurait envie de changer ses objets contre ceux d'un autre agent. Cependant, il n'existe pas forcément d'allocation envy-free quand on est dans le cadre de ressources indivisibles. Afin de surmonter cette limitation, des relaxations ont été récemment proposées dans la littérature.Dans cette thèse, nous étudions tout d'abord une famille de procédures décentralisées basée sur des échanges d'objets entre agents. Nous analysons en particulier comment ces procédures se comportent et les propriétés désirables qu'elles montrent. Plus précisément, on étudie les séquences de choix sincères et les cycles d'échanges de ressources. Dans un second temps, nous proposons de nouvelles relaxations de la notion d'absence d'envie (et d'autres mesures d'équité) et les étudions en profondeur. La première relaxation a pour but d'équilibrer l'envie entre les agents (quand elle ne peut être évitée) et se base sur l'Ordered Weighted Average (OWA), un agrégateur habituellement utilisé dans le domaine de l'optimisation multicritère pour traduire l'équité. La deuxième relaxation se concentre sur l'approbation sociale de l'envie et se rapproche plus de la théorie du vote étant donné que les agents votent sur l'envie des autres agents. Nous examinons les aspects computationnels liés à ces nouvelles relaxations, leurs liens avec des notions d'équité et d'efficacité existantes avant de les tester expérimentalement
The work of this thesis is in the scope of Computational Social Choice. It is a field at the intersection of Social Choice, Computer Science and Artificial Intelligence. In particular, we study the problem of Fair Division of Indivisible Goods where the the objective is to find a fair and efficient allocation of a set of (valuable) objects among a set of agents. While efficiency is usually brought by the minimal requirement of completeness (all the objects have to be allocated in order not to waste anything), or the more demanding notion of Pareto-Optimality (an allocation is Pareto-Optimal if there is no allocation such that all the agents are not worse off and one agent is strictly better off), several notions have been proposed to define the fairness of an allocation.One of the most prominent fairness measures is called envy-freeness. An allocation is said to be envy-free if no agent would like to exchange her bundle of resources with another agent. However, envy-freeness is not guaranteed to exist when considering indivisible goods so various relaxations have been proposed recently in the literature to overcome this limitation.In this thesis, we first thoroughly study a family of decentralized allocation procedures related to exchanges of goods. We analyze how these procedures behave and the desirable properties they exhibit. More specifically, we study sequence of sincere choices and cycle exchanges of resources. We then propose new relaxations of the envy-freeness notion (and also of other fairness measures) and thoroughly study them. Our first relaxation aims at balancing the envy among the agents (when it cannot be avoided) and is based on the Order Weighted Average (OWA) aggregator usually used in multi-criteria optimisation to bring fairness. The second relaxation focuses on the social approval of the envy and is more related to voting theory, as it lets agents vote about the envy of the other agents. We investigate computational issues related to these new relaxations, their link with existing fairness and efficiency notions and we experimentally test them

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Reis, Vinícius Ataídes. „Proposta de um ambiente multiagente para a monitoração analítica de indicadores gerenciais e de apoio à decisão para uma empresa do setor elétrico“. Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8544.

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Decision support and management indicator are fundamental inputs to make good planning and, for consequence, a good management of the operational processes in a company. Nowadays, the electrical sector companies in the Brazil have almost all the operational activities automated. However, the monitoring of all these activities is manual and requires a lot of time for manipulation and preparation by the managers of the information. This work proposes a solution based in a multiagent environment to create an analytical monitoring system. The solution aims to verify the performance of an organizational process from automatic analyzes of its indicators. The monitoring permits identify problems and apply corrective actions as soon as possible. To implement the proposed solution, this work proposes a customization of the management indicator system of the company. The paper shows the proposed multiagent architecture to execute the performance analyzes of the processes. Beside this, the paper shows the computational implementation as from evolutionary maintenances in the management indicator system. To show the effectiveness of the created solution, this paper presents some examples of how to create an analytical monitoring in it. Two case studies are carried out, detailing the construction of the solution and the monitoring process in the environment, allowing the reader to evaluate how much the proposed approach is efficient. The practical results obtained demonstrate the success of the work. Finally, work concludes that proposed approach reaches the project objectives.
Os indicadores de gestão e de suporte à decisão são insumos fundamentais para o bom planejamento e, consequentemente, para uma boa gestão dos processos de trabalho de uma organização. As empresas do setor elétrico no Brasil têm quase todas as suas atividades operacionais automatizadas. No entanto, o acompanhamento de grande parte dessas atividades é realizado de forma manual e demanda muito tempo de manipulação e preparação pelos gestores das informações. Este trabalho provê uma solução baseada em um ambiente multiagente para criar um sistema de monitoramento analítico. A solução visa verificar o desempenho de um processo organizacional a partir de análises automáticas de seus indicadores. O monitoramento permite identificar problemas, propiciando a oportunidade de aplicar ações corretivas o mais rápido possível. Para implementar a solução proposta, este trabalho propõe uma personalização do sistema de indicadores de gestão da empresa. O documento mostra a arquitetura multiagente proposta para executar as análises de desempenho dos processos. Além disso, mostra a implementação computacional a partir de manutenções evolutivas no sistema de indicadores de gerenciamento. Para mostrar a eficácia da solução criada, este trabalho apresenta exemplos de como criar um monitoramento analítico na solução criada. Dois estudos de caso são realizados, detalhando a construção da solução e o monitoramento das mesmas no ambiente, permitindo ao leitor avaliar o quanto a abordagem proposta é eficiente. Os resultados práticos obtidos demonstram o sucesso do trabalho. Finalmente, o trabalho conclui que a abordagem proposta atende aos objetivos do projeto.

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Moreira, Leonardo Henrique. „Modelo de planejamento multiagente com verificação, transformação e validação de planos“. reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/31362.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.
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Planejamento multiagente é um tema desafiador e atual na área de pesquisa de Inteligência Artificial. A complexidade e necessidade de interação entre os agentes tem como consequência elevados valores para métricas relacionadas ao tempo de execução e volume de comunicação. Considerando esses fatores como interessantes questões de pesquisa, esse trabalho propõe um modelo de planejamento multiagente com verificação, transformação e validação de planos. O modelo proposto foi denominado LCMAP - Lightweigth Coordination Multiagent Planning, por utilizar um modelo de coordenação eficiente reduzindo o nível de troca de mensagens entre os agentes. No LCMAP a verificação e transformação do problema são realizadas em uma fase inicial, com o intuito de facilitar o processo de planejamento diminuindo a interação entre os agentes. As capacidades dos agentes são analisadas quanto à possibilidade de atingirem os objetivos de forma total ou parcial. Para simplificar o problema original, o mesmo é transformado em um conjunto de instâncias menores através da atribuição dos objetivos aos agentes com as capacidades necessárias. Desta forma, o processo de planejamento é realizado de maneira individual, minimizando o tempo necessário e o volume de mensagens trocadas. Por fim, os planos calculados são validados, garantindo que suas execuções possam ocorrer de maneira paralela. As principais contribuições do trabalho incluem: (i) realização de testes antes da fase de planejamento para avaliar a possibilidade de encontrar uma solução; (ii) utilização de técnicas de execução paralela, sendo este aspecto pouco explorado nos trabalhos relacionados; e (iii) alocação eficiente dos recursos disponíveis através de estratégias que visam minimizar o número de agentes ou balancear a carga entre os mesmos.
Multiagent planning is a challenging and modern theme in the Artificial Intelligence research area. The complexity and requirements of interactions among agents lead to high values of metrics related to execution time and communication volume. Concerning these points as interesting research questions, this work proposes a multiagent planning model with plan verification, transformation, and validation. The proposal was defined as LCMAP - Lightweight Coordination Multiagent Planning due to its use of an efficient coordination process that aims to reduce the message exchanged level among agents. In LCMAP, the problem verification and validation are carried out in an initial phase in order to ease the planning process by diminishing agent interactions. The agent’s capabilities are analyzed about the possibility of reaching the goals in a total or partial way. The original problem is transformed to smaller instances set by assigning goals to agents with the required capabilities. In this sense, the planning process is performed in an individual way, minimizing the necessary time and the message exchanged volume. Finally, the plans found are validated in order to grant that their executions can occur parallel. The main contributions of these works include: (i) tests carried out before the planning phase in order to evaluate the possibility of finding a solution; (ii) use of parallel execution techniques, which are rarely exploited in related works; and (iii) efficient allocation of available resources using strategies that aim to minimize the number of agents or balance the load among them.

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Knapek, Petr. „Multiagentní podpora pro vytváření strategických her“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-413301.

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This thesis is dedicated to creating a new system with capabilities to create new generic, autonomous strategy computer game controlling elements based on multi-agent systems with social, intelligent decision-making and learning skills. Basic types of strategy games and problems of their playing will be introduced, along with currently used methods of intelligent game AI development. This thesis also presents design and implementation of the new system, working model for a specific game and results obtained while testing it.

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Paton, Helen Victoria. „Decision making in a multi-agency team“. Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/decision-making-in-a-multiagency-team(fd4fea35-aea1-4717-b671-7a94a18361ef).html.

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Every time a practitioner in children’s services offers a child and their family an intervention a decision or decisions has to be made. However the decision making process in children’s services has rarely been studied. Decision making has been extensively studied in other disciplines in both laboratory and real life situations. A dual process model has been proposed consisting of a fast, automatic, intuitive system and a slower reflective system. The two systems are deemed to work best in different situations and have their own strengths and weaknesses. Decision making in complex situations, such as those involving children and families, involves both types of processes but checks and balances help to ensure that the process is optimal. Expertise can develop over time through reflection on the process. This study explores the decision making process in a Targeted Mental Health in Schools Team (TaMHS) in one Local Authority. TaMHS is a three year Department for Children, Schools and Families’ pathfinder programme aiming ‘to improve mental health outcomes for children and young people via interventions delivered through school’ (DCSF, 2008d). Substantial changes have taken place within children’s services over recent years and research has explored the facilitators of and barriers to effective multi-agency working. However lack of clarity in terminology and detail has prevented an evaluation of the causal links between the facilitators and better outcomes for children and young people. I have used a case study approach with a multi-agency team which has practitioners from six professional backgrounds. Interview data from the manager and six practitioners and an observation of one of their cluster meetings has been collected and analysed using thematic analysis. I have developed a rich picture of the decision making process (DMP) in this team. The DMP is a complex, iterative process which is facilitated by a predetermined organisational structure and continues throughout the assessment and intervention stages. Diversity of views is welcomed and different perspectives are merged leading to shared decisions. Families and school staff are fully involved. Practitioners seem to use processes below conscious awareness as well as a more explicit process which links explanatory models, chiefly risk and resilience, with the choices of interventions. I have identified that many of the known facilitators for effective multi-agency working exist within this team and I propose that these could be the mechanisms which trigger effective decision making. I suggest that the group process involved in this team could be useful for other teams in children’s services. I also discuss ways to improve decision making and I have created a DMP Attributes Model which I have described and then discussed as a tool to aid professional development through the supervision process for practitioners within children’s services. I explore a possible role for educational psychologists in this process. Future research could study the usefulness of this tool with practitioners.

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Fonseca, Luís Carlos Costa. „SISTEMA MULTIAGENTES PARA NEGOCIAÇÃO NO AMBIENTE ICS DE COMÉRCIO ELETRÔNICO“. Universidade Federal do Maranhão, 2003. http://tedebc.ufma.br:8080/jspui/handle/tede/325.

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This work is part of a major project called ICS (Intelliget Commerce System) that has as it´s main goal to develop an Intelligent Negotiation System for Eletronic Business on the B2B category wich is being developed at UFMA (Maranhão s Federal University) under the Coordination of Prof. Dr. Sofiane Labidi. In this work we will specifically see the basic architecture proposed for the system, the technologies that bases it and in a more detailed way we will see the negotiation between sofware agents beyond its applications in the proposed system. In this context, we intend, to propose and develop a system that can automatize the negotiation mechanisms - purchase and sale of products and services - making the interactions between companies faster, more sophisticated and efficient, and thus increase even more the profits of this business. Being so, we present the ICS (Intelligent Commerce Sysrtem) as an Eletronic Business System based on Mobile Software Agent s technology that follows the OMG MASIF2 of OMG standard(OMG, 2000). Three important features of ICS are emphasized: the eletronic commerce lifecycle, the user modeling and the proposed ontologies on each of the lifecycle´s phase. Thus the ICS aims to get as end item of the negotiation, the best opportunities of purchase and sale of products and services, thus providing, a decision making support system.
Este trabalho faz parte de um projeto maior chamado ICS (Intelliget Commerce System) e que tem como objetivo desenvolver um Ambiente Inteligente de Negociação para Comércio Eletrônico na categoria B2B e que está sendo desenvolvido na UFMA (Universidade Federal do Maranhão) sob a Orientação do Prof. Dr. Sofiane Labidi. Neste trabalho trataremos especificamente da arquitetura básica proposta para o sistema, as tecnologias que o fundamentam e de forma mais detalhada trataremos da negociação entre agentes de software além das suas aplicações no sistema ora proposto. Neste contexto, pretendemos, propor e implementar um sistema que possa automatizar os mecanismos de negociação - compra e venda de produtos e serviços - tornando as interações entre empresas mais sofisticadas, rápidas e eficientes, e assim incrementar ainda mais os lucros deste ramo de negócio. Sendo assim, nós apresentamos o ICS (Intelligent Commerce Sysrtem) como um sistema de Comércio Eletrônico baseado na tecnologia de Agentes Móveis seguindo o padrão MASIF1 da OMG (OMG, 2000). Três importantes características do ICS são enfatizadas: ciclo de vida do comércio eletrônico, modelagem do usuário e as ontologias propostas para cada fase do ciclo de vida. Assim o ICS visa obter como produto final da negociação, as melhores oportunidades de compra e venda de produtos e serviços, provendo assim, um sistema de suporte à tomada de decisão.

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FARIAS, Osevaldo da Silva. „MODELAGEM E IMPLEMENTAÇÃO DE UM SISTEMA MULTIAGENTE PARA SELEÇÃO DE FALHAS E TOMADA DE DECISÃO EM VIRADORES DE VAGÕES“. Universidade Federal do Maranhão, 2009. http://tedebc.ufma.br:8080/jspui/handle/tede/1831.

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FAPEMA, CAPES
This work leads to the modeling and implementation of a multi-agent application of faults selection and decision making for VALE’s rotary railcar dumpers. The work explore development approaches and knowledge-based achtectures aiming to SADDEM build, a multi-agent system applyed to decision making in ore unload cells. The particularities of cognitives agents structures are based on Jess inference engine in order to link decisions and maintain plans recommendations made by the system. The ontology is developed throught elicitation of information and knowledge during interviews and VV311-K01 car positioner set operating activities. The artifacts produced during the development clycle phases employs PASSI methodology technics, Artificial Intelligence and support engineering to software development using JADE framework. The real time module is linked to the software of supervision and monitoring of the unloading cell productions of VALE.
Este trabalho apresenta a modelagem e implementação de um aplicação multiagente para a seleção de falhas e tomada de decisão em viradores de vagões da VALE. A proposta investiga abordagens de desenvolvimento e arquiteturas baseadas no conhecimento para criação do SADDEM, um sistema multiagente aplicado ao apoio a decisão em células de descarga de minério. As particularidades das estruturas cognitivas de agentes são baseadas no mecanismo inferência Jess, objetivando encadear decisões e recomendações de planos de manutenção feitas pelo sistema. A ontologia do sistema é elaborada por meio das informações e conhecimento eliciados durante entrevista e atividades operacionais referentes ao conjunto carro posicionador observadas no equipamento virador de vagões VV311- K01. Os artefatos produzidos durante as fases do ciclo de desenvolvimento têm a aplicação das técnicas da metodologia PASSI, Inteligência Artificial e das engenharias de apoio ao desenvolvimento de software com uso do framework JADE. O módulo de tempo real do sistema está vinculado ao software de supervisão e monitoramento das células de produção do setor de descarga de minério da VALE.

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Oliveira, Valdir Leanderson Cirqueira de. „Sistema de apoio a decisão baseado em agentes para gestão de sistemas de produção distribuídos: aplicação em campos de petróleo terrestres“. Escola Politécnica / Instituto de Matemática, 2013. http://repositorio.ufba.br/ri/handle/ri/21336.

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Os sistemas de manufatura são em geral caracterizados por se apresentarem divididos em diversos subsistemas inter-relacionados. Cada um deles tem um papel específico e todos devem cooperar para alcançar as metas pré-estabelecidas. A depender da organização, os sistemas serão mais ou menos hierarquizados ou distribuídos. Os campos de produção de petróleo são exemplos de sistemas de manufatura de natureza distribuída, tendo em seus componentes (poços, estações de coleta, estações de tratamento, etc.), representações modulares com características bastante peculiares. As atividades das equipes de operação no campo de petróleo muitas vezes envolvem situações de alta periculosidade, pois lidam com problemas críticos de diversas naturezas, que ocorrem em locais de difícil acesso e precisam de soluções urgentes, associadas algumas vezes a restrições de mobilidade e comunicação. Este trabalho apresenta o SGCP - Sistema de Gerenciamento de Campos de Petróleo - um sistema de software baseado em agentes computacionais que auxilia na realização de diagnósticos de problemas relacionados à produtividade de cada componente do campo e em análises de desempenho tendo como base a rentabilidade. Os agentes executam tarefas agregando valor aos materiais processados (óleo, gás natural, água produzida) e transferem custos entre si como uma cobrança pelo serviço prestado. No estudo de caso é apresentado como os problemas de produção são identificados através da ação dos agentes que, utilizando técnicas de controle estatístico de processo, monitoram seu ambiente e enviam alertas

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Cruciol, Leonardo Luiz Barbosa Vieira. „Modelagem de apoio à decisão para o problema de espera no ar utilizando sistemas multiagentes e aprendizagem por reforço“. reponame:Repositório Institucional da UnB, 2012. http://repositorio.unb.br/handle/10482/10808.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas Departamento de Ciência da Computação, 2012.
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No domínio de gerenciamento de trafego aéreo existe o problema de espera no ar, o qual acontece quando aeronaves precisam sofrer medidas restritivas enquanto estão em vôo. Pode ocorrer devido ao fechamento do aeroporto, reorganização do fluxo de tráfego ou setores do espaço aéreo congestionados. Logo, as aeronaves em rota precisam descer com o maior nível de segurança possível e, se possível, acarretando os menores impactos no ambiente. Essas atividades são realizadas por controladores de vôo, os quais são responsáveis por verificar o cenário aéreo, analisar possíveis situações de risco, verificar o impacto e aplicar as melhores medidas restritivas. Porém, por melhor que seja o especialista, atuar em um ambiente de tempo real, o que significa detectar, analisar e agir dentro de um intervalo de tempo extremamente reduzido, sem meios que possam efetivamente auxiliar o controlador de vôo no processo de tomada de decisão é um fator a mais de risco para o domínio. Esta pesquisa realizou a modelagem do problema de espera no ar, propôs uma solução e aplicou em dois estudos de casos, utilizando o espaço aéreo do Brasil. O primeiro na FIR-Brasília e FIR-Curitiba e o segundo na FIR-Brasília e FIR-Recife. O modelo, denominado como Air Holding Problem Module, utiliza Sistemas Multiagentes e Aprendizagem por Reforço, na qual foi utilizado o algoritmo Q-Learning para o processo de aprendizado. O sistema pode sugerir ações mais precisas e realistas, baseadas no passado e no conhecimento dos especialistas, prevê o impacto das medidas restritivas sob o cenário local e/ou global e, assim, aumentar o nível de segurança do cenário aéreo, pois ao agir os controladores de vôo já possuem uma previsão dos seus impactos. Os resultados alcançados se mostraram acima dos esperados inicialmente e obtiveram melhores resultados conforme os cenários se tornavam mais críticos. Avaliando os estudos de caso, o primeiro experimento alcançou entre 8% e 47% de melhoria nos cenários locais e entre 0% e 49% de melhoria no cenário global, ou seja, o pior resultado foi igual a abordagem tradicional; no segundo, foi alcançado entre 15% e 57% de melhoria nos cenários locais e entre 41% e 48% de melhoria no cenário global. Além da melhoria no processo de análise, previsão de impactos e sugestões de medidas restritivas, foi possível modelar uma solução computacional para apoiar, de forma efetiva, a tomada de decisão do controlador de vôo. _________________________________________________________________________________ ABSTRACT
In the air traffic management domain there is the air holding problem, which happens when restrictive measures have to be taken while in the flight. This problem may occur due to airport closure, traffic flow reorganization or congested airspace sectors. In this case, aircrafts in flight must land with the highest level of safety possible and, if possible, resulting in the lowest impacts in environment. These activities are performed by flight controllers, which are responsible to confirm airspace scenario and analyze possible risk situations. Therefore, even with a great specialist, take decision in a real time environment which means detect, analyze and act in a time interval extremely short, without any means to support effectively the flight controller in the decision making process is one more factor of risk for the domain. This research accomplished the modeling of air holding problem, offered a solution and applied it in two study cases in Brazil airspace. The first one in FIR-Brasilia and FIR-Curitiba and second one in FIR-Brasilia and FIR-Recife. The model defined as Air Holding Problem Module, uses Multiagent System and Reinforcement Learning with the Q-Learning algorithm for the learning process. The system can suggest more accurate and realistic actions, based on the past and knowledge of the specialists, predict the impact of restrictive measures under the local and global scenario, and then, increase the safety level in the airspace scenario. The results achieved were better as scenarios became more critical. Evaluating the study cases, the first one reached between 8% to 47% of improvement of the level of the local air traffic scenario and between 0% to 49% of improvement in the global scenario. The second one achieved between 15% and 57% improvement in local scenarios and between 41% and 48% improvement in global scenario. Besides the improvement of the analysis process, prediction of impacts and suggestions of restrictive measures, it was possible to create a computational solution to support, in an effective way, the process of decision making of the flight controller.

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García, Fernández Luis Amable. „Diseño e Implementación de una Arquitectura Multiagente para la Ayuda a la Toma de Decisiones en un Sistema de Control de Tráfico Urbano“. Doctoral thesis, Universitat Jaume I, 2000. http://hdl.handle.net/10803/10376.

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El control de Tráfico Urbano es altamento complejo y dinámico: depende de la cantidad, tipo y calidad de los datos recibidos, de las previsiones de datos a recibir, del comportamiento del tráfico (actual, pasado y en previsión), el conocimiento del control a aplicar y del entorno de actuación. En esta tesis doctoral se analizan las diferentes aproximaciones realizadas desde el ámbito de la Inteligencia Artificial para identificar las principales debilidades de estos sistemas. Como resultado de este análisis se propone una nueva aproximación basada en la integración de datos procedentes de distintos sensores (mediante la adopción de un modelo cualitativo de datos), el análisis en tiempo real de la situación del tráfico (mediante un simulador macroscópico cualitativo basado en la densidad de vehículos en cola a la entrada de las intersecciones), la identificación de problemas actuales y potenciales de tráfico (mediante un análisis temporal cualitativo de la evolución de la densidad de vehículos) y la sugerencia de ejecución de acciones de control de tiempos de rojo para reducir las congestiones de tráfico actuales, evitar que evolucionen a situaciones de colapso y evitar congestiones futuras. Se define y diseña un prototipo con arquitectura multiagente que integra las características mencionadas. Su implementación se realiza en un sistema distribuido COTS. La ejecución del prototipo en pruebas de laboratorio (con datos reales de la ciudad de Castellón de la Plana) proporciona resultados que avalan la aproximación realizada.

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Amorim, Leonardo Afonso. „Agente para suporte à decisão multicritério em gestão pública participativa“. Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4125.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Decision making in public management is associated with a high degree of complexity due to insufficient financial resources to meet all the demands emanating from various sectors of society. Often, economic activities are in conflict with social or environmental causes. Another important aspect in decision making in public management is the inclusion of various stakeholders, eg public management experts, small business owners, shopkeepers, teachers, representatives of social and professional classes, citizens etc. The goal of this master thesis is to present two computational agents to aid decision making in public management as part of ADGEPA project: Miner Agent (MA) and Agent Decision Support (DSA). The MA uses data mining techniques and DSA uses multi-criteria analysis to point out relevant issues. The context in which this work fits is ADGEPA project. The ADGEPA (which means Digital Assistant for Participatory Public Management) is an innovative practice to support participatory decision making in public resources management. The main contribution of this master thesis is the ability to assist in the discovery of patterns and correlations between environmental aspects that are not too obvious and can vary from community to community. This contribution would help the public manager to make systemic decisions that in addition to attacking the main problem of a given region would decrease or solve other problems. The validation of the results depends on actual data and analysis of public managers. In this work, the data were simulated.
Tomada de decisão em gestão pública é associada ao alto grau de complexidade devido à insuficiência de recursos financeiros para atender todas as demandas provindas de diversos setores da sociedade. Frequentemente, atividades econômicas estão em conflito com causas sociais ou ambientais. Outro aspecto importante em tomadas de decisão em gestão pública é a inclusão dos diversos stakeholders, por exemplo especialistas em gestão pública, pequenos empresários, pequenos comerciantes, professores, representantes de classes sociais e profissionais, os próprios cidadãos etc. Diante disto, o objetivo deste trabalho de mestrado é apresentar uma proposta de Agente Minerador (AM) e Agente de Suporte à Decisão (ASD) para Gestão Pública Participativa e como fazer a interface entre eles. O AM faz uso de técnicas de mineração de dados para se encontrar regras de associação entre dados socioambientais, temporais e espaciais e o ASD faz uso de análise multicritério para ranquear problemas socioambientais que devem ser solucionados com prioridade. O contexto em que este trabalho se insere é o projeto ADGEPA (Assistente Digital para Gestão Pública Participativa), um projeto inovador para suporte à tomada de decisão participativa em gestão pública. Entende-se que a contribuição principal deste trabalho de mestrado é a possibilidade de auxiliar na descoberta de padrões e correlações entre aspectos socioambientais que não são muito óbvias e que podem variar de comunidade para comunidade. Esta contribuição poderá auxiliar o gestor público a tomar decisões sistêmicas que além de atacar o problema principal de uma determinada região diminuirá ou solucionará também problemas de outros aspectos. A validação dos resultados depende de dados reais e de análise de gestores públicos. Neste trabalho os dados foram simulados.

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Rossetti, Rosaldo Jose Fernandes. „A BDI-based approach for the assessment of driver's decision-making in commuter scenarios“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2002. http://hdl.handle.net/10183/5595.

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O rápido crescimento das regiões urbanas tem impacto significativo nos sistemas de tráfego e transportes. Políticas de gerenciamento e estratégias de planejamento alternativas são claramente necessárias para o tratamento da capacidade limitada, e cada vez mais deficitária, das redes viárias. O conceito de Sistemas Inteligentes de Transportes (ITS) surge neste cenário; mais do que procurar aumentar a capacidade por meio de modificações físicas na infraestrutura, sua premissa baseia-se na utilização de tecnologias avançadas de comunicação e computação para melhor gerir os recursos de tráfego e transportes atuais. Influenciar o padrão do comportamento dos usuários é um desafio que tem estimulado muita pesquisa na área de ITS, onde fatores humanos passam a ter grande importância na modelagem, simulação e avaliação dessa abordagem inovadora. Este trabalho tem como foco a utilização de Sistemas Multiagentes (MAS) na representação dos sistemas de tráfego e transporte, com base nas novasmedidas de desempenho impostas pelas tecnologias ITS. As características de agentes têm grande potencial para representar componentes geográfica e funcionalmente distribuídos, como a maioria dos elementos no domínio da aplicação. Uma arquitetura BDI (beliefs, desires, intentions) é apresentada como alternativa a modelos tradicionais, usados para representar o comportamento do motorista em simulação microscópica, considerando-se a representação explícita dos estados mentais dos usuários. Os conceitos básicos de ITS e MAS são apresentados, assim como exemplos de aplicações relacionados com o tema do trabalho. Esta foi a motivação para a extensão de um simulador microscópico existente, no sentido de incorporar as características dos MAS para melhorar a representação dos motoristas. Assim, a demanda é gerada a partir de uma população de agentes, resultando da decisão sobre a rota e o tempo de partida ao longo de vários dias. O modelo estendido, que passa a suportar a interação de motoristas BDI, foi efetivamente implementado e foram executados diferentes experimentos para testar a abordagem em cenários de tráfego urbano. MAS permite uma abordagem direcionada a processos que facilita a construção de representações modulares, robustas, e extensíveis, características pouco presentes em abordagens voltadas ao resultado. Suas premissas de abstração permitem uma associação direta entre modelo e implementação. Incerteza e variabilidade são assim tratadas de maneira mais intuitiva, uma vez que arquiteturas cognitivas permitem uma fácil representação do comportamento humano na estrutura do motorista. Desta forma, MAS estende a simulação microscópica de tráfego no sentido de melhor representar a complexidade inerente às tecnologias ITS.
The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.

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Meinerz, Giovani Volnei. „DECOLA : um modelo de decisões colaborativas para sequenciamentos de pousos e decolagens utilizando sistemas multiagentes“. Instituto Tecnológico de Aeronáutica, 2011. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1971.

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A infraestrutura do sistema de transporte aéreo brasileiro e mundial vem sofrendo nos últimos anos pressão contínua e crescente, devido às grandes solicitações a que é submetida. Este cenário pode levar setores de ATC em Áreas de Controle Terminal, TMA, a condições de congestionamento e saturação. Diante da limitada capacidade de expansão dos recursos de infraestrutura, as alternativas de solução encontram-se voltadas para a necessidade de melhoria das atividades de Gerenciamento de Fluxo de Tráfego Aéreo, ATFM, envolvendo a fase de planejamento pré-tática e a forma de ATM pró-ativa. Neste sentido, o atual desafio é conceber soluções que ofereçam suportes mais apropriados ao processo de tomada de decisão, conferindo maior segurança e racionalidade às operações de tráfego aéreo. Este trabalho de pesquisa apresenta um modelo de decisões colaborativas para sequenciamentos de pousos e decolagens, utilizando a técnica de SMA, em conjunto com os conceitos da arquitetura de Crenças, Desejos e Intenções, BDI. A análise e avaliação dos resultados obtidos mostra que a aplicação destas abordagens contribui para a melhoria da eficiência do sequenciamento do fluxo de aeronaves em uma TMA, ajustando a demanda à capacidade, evitando desbalanceamentos.

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Luo, Hang. „Influence in combinatorial and collective decision-making : by the example of UN security council voting“. Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066186/document.

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L'objet de cette thèse (bi-disciplinaire, en informatique et sciences politiques) est l'étude de phénomènes d'influence dans des situations de prise de décision collective, impliquant plusieurs sujets interdépendants, ce qui amène à considérer des domaines dits combinatoires. L'importance de ces phénomènes d'influence est observée depuis de nombreuses années dans différents domaines (dont les sciences politiques), mais leur modélisation reste peu développée jusqu'à présent. En se basant sur un modèle existant intégrant la prise en compte de l'influence à un langage de représentation des préférences ordinal, le premier travail de cette thèse a été d'identifier des schémas d'influences pouvant survenir, entre sujets et/ou agents différents. Une deuxième contribution de cette thèse est la prise en compte de domaines partiels (permettant de représenter contraintes exogènes et abstentions, par exemple), une nécessité dans les situations envisagées. De même, différents modèles visant à capturer l'influence simultanée de plusieurs agents sont présentés et discutés. Dans un deuxième temps, l'exemple des votes au conseil de sécurité del'ONU est utilisé: l'occurence de ces schémas est envisagée à l'aide d'une analyse qualitative de différentes séquences de votes (sciences politiques), et de la recherche d'indices de schémas similaires dans les données de vote accessibles. Enfin, le modèle est mobilisé dans le cadre d'une simulation multi-agents (réalisée en Netlogo), et visant à tester différentes réformes envisagées du conseil de sécurité
Influence study in combinatorial and collective decision-making, is an interdisciplinary research area combining computer science and social science, especially artificial intelligence and collective decision-making. Influence has long been studied, for instance in political science, but in the context of combinatorial and collective decision-making, this calls for a study of how influences works among multi-agents and multi-issues, how influences and decision-making are interleaved, and how the structures of influence among agents and issues produce an effect. In the thesis, we mainly performed three aspects of work:Firstly, build complex models of influence based on preference representation languages and social influence models, proposed a series of new patterns of influence to better describe the complex influences in real-world situation, and discussed a series of theoretical problems of influencing and influenced structure, influence from more than one origins, and influence with abstentions and constraints. We then test the models of influence from an exemplary perspective for interdisciplinary study, from both social science and computer science paradigms, by both qualitative case studies approach and quantitative matching algorithms approach, to provide an evaluation for the models of influence.Finally, we use the models of influence to perform agent-based simulations, by the example UN Security Council voting. We design those experiments from both social and computer science perspectives, implement it in Netlogo, and discuss the interleaved effects between new cases of influence and different SC reform schemes

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Schmitz, Luiz Alberto. „Uma ferramenta adaptativa para apoiar o planejamento de projetos do desenvolvimento de produtos“. reponame:Repositório Institucional da UFSC, 2013. https://repositorio.ufsc.br/handle/123456789/106859.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2013
Made available in DSpace on 2013-12-05T22:35:09Z (GMT). No. of bitstreams: 1 320118.pdf: 2222095 bytes, checksum: 48ce6ff10371e71de85bf611eea6b607 (MD5) Previous issue date: 2013
As organizações enfrentam modificações no cenário competitivo mundial, cada vez mais, frequentes, complexas e impactantes. Mudanças que são definidas pelo aperfeiçoamento e pela disseminação da informação através de diversos meios, de novas tecnologias e da consequente internacionalização, criando complexidade de relações encontrada, tipicamente, em sistemas sociotécnicos, que no caso do PDP, são os mercados, as tecnologias e os clientes. A utilização da adaptatividade, um método de modelar sistemas, cada vez mais presente, possibilita a percepção instantânea da necessidade dinâmica e atualizada dos interessados no processo. A pesquisa inicia-se pela busca de informações que servem de base para o modelo a ser desenvolvido, formando os elementos necessários ao modelo adaptativo. O sistema adaptativo vale-se de árvores de decisões adaptativas, que o tornam útil a diversas abordagens. A modelagem multiagentes é utilizada tanto para a percepção de cenários do ambiente de entrada do PDP, quanto para a avaliação do modelo, inserindo situações não lineares no simulador. O atendimento dos princípios enxutos é realizado através de uma objetividade mensurável que agrega valor ao processo, através da minimização dos desperdícios. Modelos padronizados para o desenvolvimento de produtos, através de processos, emergem como uma solução cada vez mais utilizada. O problema da adaptação de um modelo de referência pode ser visto pela necessidade e dificuldade de simplificação, mas, também, pela dificuldade de reconhecer todas as atualizações das necessidades dos clientes. Para que o processo de desenvolvimento de produtos, como um todo, a partir do planejamento de um projeto, adquira efetivas flexibilidade e aproximação ao cliente, é proposto um modelo que forneça as condições necessárias. Isto, visando adaptar fases e atividades do processo a um projeto específico, baseando-se em estratégias que ofereçam maior dinâmica ao processo, que possui um fluxo predominante de informações, então é coerente que um estudo procure modelá-las corretamente. Os resultados são avaliados, quanto à adaptatividade e capacidade de adaptação, a partir de uma ferramenta adaptativa (software) para apoiar as decisões de adaptação a estruturas de projetos de desenvolvimento de produtos.

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Souza, Luiz Cláudio Guarita. „Regras de raciocínio aplicadas a ontologias por meio de sistema multiagente para apoio a decisões organizacionais / Luiz Cláudio Guarita Souza ; orientador, Bráulio Coelho Ávila, co-orientador, Marcos Augusto H. Shmeil“. reponame:Biblioteca Digital de Teses e Dissertações da PUC_PR, 2003. http://www.biblioteca.pucpr.br/tede/tde_busca/arquivo.php?codArquivo=3008.

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Dissertação (mestrado) - Pontifícia Universidade Católica do Paraná, 2003
Inclui bibliografias
Atualmente, as informações circulam com celeridade em organizações humanas, o que gera, internamente, dificuldades na preservação das mesmas, o que seria imprescindível para o auxílio à tomada de decisões. Os Sistemas de Apoio à Decisão (SAD), cuja função
Nowadays information circulates quickly in a human organization, what generates an internal difficulty in preserving inside the organization essential information to support decision-making. Decision Support Systems (DSS) aiding organizational decision-ma

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Luo, Hang. „Influence in combinatorial and collective decision-making : by the example of UN security council voting“. Electronic Thesis or Diss., Paris 6, 2015. http://www.theses.fr/2015PA066186.

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L'objet de cette thèse (bi-disciplinaire, en informatique et sciences politiques) est l'étude de phénomènes d'influence dans des situations de prise de décision collective, impliquant plusieurs sujets interdépendants, ce qui amène à considérer des domaines dits combinatoires. L'importance de ces phénomènes d'influence est observée depuis de nombreuses années dans différents domaines (dont les sciences politiques), mais leur modélisation reste peu développée jusqu'à présent. En se basant sur un modèle existant intégrant la prise en compte de l'influence à un langage de représentation des préférences ordinal, le premier travail de cette thèse a été d'identifier des schémas d'influences pouvant survenir, entre sujets et/ou agents différents. Une deuxième contribution de cette thèse est la prise en compte de domaines partiels (permettant de représenter contraintes exogènes et abstentions, par exemple), une nécessité dans les situations envisagées. De même, différents modèles visant à capturer l'influence simultanée de plusieurs agents sont présentés et discutés. Dans un deuxième temps, l'exemple des votes au conseil de sécurité del'ONU est utilisé: l'occurence de ces schémas est envisagée à l'aide d'une analyse qualitative de différentes séquences de votes (sciences politiques), et de la recherche d'indices de schémas similaires dans les données de vote accessibles. Enfin, le modèle est mobilisé dans le cadre d'une simulation multi-agents (réalisée en Netlogo), et visant à tester différentes réformes envisagées du conseil de sécurité
Influence study in combinatorial and collective decision-making, is an interdisciplinary research area combining computer science and social science, especially artificial intelligence and collective decision-making. Influence has long been studied, for instance in political science, but in the context of combinatorial and collective decision-making, this calls for a study of how influences works among multi-agents and multi-issues, how influences and decision-making are interleaved, and how the structures of influence among agents and issues produce an effect. In the thesis, we mainly performed three aspects of work:Firstly, build complex models of influence based on preference representation languages and social influence models, proposed a series of new patterns of influence to better describe the complex influences in real-world situation, and discussed a series of theoretical problems of influencing and influenced structure, influence from more than one origins, and influence with abstentions and constraints. We then test the models of influence from an exemplary perspective for interdisciplinary study, from both social science and computer science paradigms, by both qualitative case studies approach and quantitative matching algorithms approach, to provide an evaluation for the models of influence.Finally, we use the models of influence to perform agent-based simulations, by the example UN Security Council voting. We design those experiments from both social and computer science perspectives, implement it in Netlogo, and discuss the interleaved effects between new cases of influence and different SC reform schemes

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Adamatti, Diana Francisca. „Inserção de jogadores virtuais em jogos de papéis para uso em sistemas de apoio à decisão em grupo: um experimento no domínio da gestão de recursos naturais“. Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-07012008-154915/.

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Este trabalho propoe a definicao de uma arquitetura para incorporar jogadores virtuais em uma classe de Sistemas de Apoio a Decisao em Grupos (SADs-G), baseados na utilizacao conjunta de Simulacao Baseada em Multiagentes (MABS) e Jogos de Papeis (RPGs). Estas duas tecnicas, MABS e RPGs, vem sendo utilizadas de forma integrada ha alguns anos devido a capacidade de discussao e aprendizado dos RPG e da capacidade dinamica de MABS. Os jogadores virtuais definidos devem ter a capacidade de tomada de decisao e de comunicacao entre si e com os jogadores reais durante as negociacoes. Os principais aspectos discutidos neste trabalho sao: i) se os jogadores virtuais definidos possuem comportamentos nao-triviais perante aos jogadores reais; ii) se continua ocorrendo o processo de negociacao entre os jogadores, sejam eles reais ou virtuais; iii) se a realizacao de um jogo de forma eletronica (via computador) dificulta a interacao entre os jogadores. De forma a discutir estes aspectos, duas instancias de SADs-G foram implementadas no contexto da Gestao de Recursos Naturais, pois neste dominio o processo de negociacao e extremamente importante e complexo.
This thesis proposes the definition of an architecture to insert virtual players in a particular subclass of Group Decision Support Systems (GDSS), that uses Multi-Agent-Based Simulation (MABS) and Role-Playing Games (RPGs) techniques in an integrated way. These techniques can bring interesting results, since it is possible to join the dynamic capacity of MABS with the discussion and learning capacity of RPGs. The defined virtual players must make decisions and communicate with each other and with the real players during the negotiation process. In this work, the main aspects discussed are the following: i) can virtual players have no-trivial behavior in the face of real players? ii) is the negotiation process between all players (virtual or real) still happening when virtual players are inserted? iii) do electronic games make more difficult the interaction between players? In order to discuss these aspects, two instances of GDSSs were developed in the natural resources management domain. This domain was chosen because its negotiation process is both very important and complex.

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Banaszewski, Roni Fabio. „Modelo multiagentes baseado em um protocolo de leilões simultâneos para aplicação no problema de planejamento de transferências de produtos no segmento downstream do sistema logístico brasileiro de petróleo“. Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/822.

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CAPES
O segmento downstream da cadeia de suprimentos da indústria brasileira de petróleo é composta por bases de produção (e.g. refinarias), armazenamento (e.g. terminais) e consumo (e.g. mercados consumidores) e modais de transportes (e.g. oleodutos, navios, caminhões e trens). O planejamento da transferência de derivados de petróleo nesta rede multimodal é um problema complexo e atualmente é realizado para um horizonte de três meses com base na experiência de profissionais e sem auxílio de um sistema computacional de apoio à decisão. Basicamente, o problema pode ser visto como uma negociação para alocação de recursos disponíveis (tais como derivados de petróleo, tanques e modais de transporte) pelas diferentes bases envolvidas que necessitam enviar ou receber derivados de petróleo. Na literatura, alguns problemas semelhantes, porém mais voltados para o planejamento de redes formadas por um único tipo de modal de transporte, têm sido tratados por diferentes abordagens, com predominância da programação matemática. Estes trabalhos ilustram a difícil tarefa de modelar grandes problemas por meio desta abordagem. Geralmente, tais trabalhos consideram apenas um curto horizonte de planejamento ou apenas uma parte do problema original, tal como uma parte da rede petrolífera brasileira, gerando limitações importantes para os modelos desenvolvidos. Devido às características do problema em estudo, o qual envolve toda a rede de transporte e apresenta perfil de negociação entre as diferentes entidades envolvidas, surge o interesse da utilização do paradigma de sistemas multiagente. O paradigma de agentes tem sido aplicado a problemas de diferentes contextos, particularmente em problemas de gerenciamento de cadeias de suprimentos devido à sua correspondência natural com a realidade e, em geral, em problemas que envolvem a competição por recursos por meio de mecanismos de negociação com base em leilões. Este trabalho apresenta um novo protocolo de negociação baseado em leilões e aplicação deste protocolo em forma de um modelo multiagente na resolução do problema de planejamento em questão. Os agentes que formam a solução representam principalmente os locais de produção, armazenamento, consumo e os modais de transporte na rede petrolífera brasileira. O objetivo destes agentes é manter um nível de estoque diário factível de cada produto em cada local por meio de transferências de produtos pela rede petrolífera brasileira com preferível redução do custo de transporte. Por fim, este trabalho apresenta a satisfação destes objetivos por meio de experimentos em cenários fictícios e reais da rede brasileira de petróleo.
The Brazilian oil supply chain is composed by oil refineries, consumer markets, terminals for intermediary storage and several transportation modals, such as pipelines, ships, trucks and trains. The transportation planning of oil products in this multimodal network is a complex problem that is currently performed manually based on expertise, for a period of three months, due to the lack of a software system to cover the problem complexity. Such problem involves the negotiation of available resources such as oil products, tanks and transportation modals between different sources and consumption points. Similar problems, but more directed to the planning of single modes of transportation, have been treated by different approaches, mainly mathematical programming. Such works illustrate the difficult task of modeling large problems with this mechanism. Generally, they consider a short horizon planning or only part of the original problem, such as a part of the network, rendering important limitations to the models developed. Due to the characteristics of the problem in study where the full network needs to be considered and there exists negotiation amongst the different entities involved, the usage of multi-agent models seems to be worth to explore. Such models have been applied in different contexts such as to supply chain problems due its natural correspondence with the reality. Furthermore, in problems involving competition for resources, multi-agents negotiation mechanisms based on auctions are commonly applied. Thus, this thesis presents one auction-based solution formed by the cooperation among agents for them to achieve their goals. The agents involved in the auctions represent mainly the production, storage and consumption locations. Their goal is to maintain a daily suitable inventory level for each product by means of transportation through the multimodal network at a low transport cost. Finally, this paper presents the satisfaction of these objectives through experiments on real and fictional scenarios of Brazilian oil network.

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Berger, Luiz Marcelo. „Um modelo baseado em agentes para estudo das propriedades emergentes decorrentes da aplicação da lei penal“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/13831.

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O presente estudo busca desenvolver um modelo baseado em agentes para estudar em ambiente simulado baseado em sistemas computacionais as propriedades emergentes decorrentes da aplicação e execução da lei penal. Os modelos baseados em agentes têm-se revelado uma alternativa bastante promissora no estudo de sistemas complexos onde a grande quantidade de variáveis dinâmicas e interagentes tornam impossível o uso de técnicas puramente analíticas para a sua abordagem. Especialmente considerando que normas legais penais feitas para regular a conduta das pessoas em sociedade devem durar, a dimensão tempo acaba recebendo uma considerável relevância, o que incentiva ainda mais o uso de sistemas multiagentes, particularmente propícios nestes casos. Buscou-se construir um modelo no qual fosse possível analisar qualquer lei de natureza penal, quaisquer que fossem suas características e a quem estivesse destinada. Chegou-se, para tanto, no construto Cidadão- Estado-Oportunidade-Ambiente, que definem precisamente os contornos de qualquer conduta criminosa e que foram modelados em ambiente mutiagente. Para construir os fundamentos do comportamento dos agentes foram utilizados especialmente os referenciais teóricos de Shavell e Polinsky (2000, 2004), Becker (1968), Cohen e Felson (1979) e Clarke(1995), sem deixar de agregar outras contribuições teóricas, ainda que em escala menor. O construto, no entanto, permite que quaisquer contribuições e análises teóricas de atributos do comportamento dos agentes propostos possam ser utilizadas, uma vez que a sua generalidade permite que qualquer forma de conduta possa ser modelada. Para criar o simulador, foi utilizado o ambiente NetLogo 3.1.4 (WILENSKY,1999), cuja especial capacidade de tratar fenômenos que se desenvolvem ao longo do tempo se revelou de extrema valia e praticidade, demonstrando o potencial surgimento de propriedades imprevisíveis sob outras condições. Os testes realizados para aferir a aderência do modelo, usando distribuições de freqüência onde as médias e os desvios-padrão eram controlados, mostraram que o comportamento dos agentes em ambiente dinâmico segue perfeitamente os conceitos teóricos utilizados como referencial, especialmente no que diz respeito ao comportamento do agente cidadão, modelado segundo os conceitos estabelecidos por Becker (1968). Restou demonstrado pela simulação que a percepção de possibilidade de punição tem impacto considerável no comportamento dos agentes diante da possibilidade de delito, assim como o somatório dos custos envolvidos é grandemente influenciado pelo tipo de delito cometido, ou seja, a dimensão monetária do dano, pela estrutura de custos da justiça e pelos meios de arrecadação destes recursos utilizados pelo estado, conforme afirmam Shavell e Polinsky(2000). Para realizar os testes foram construídas tabelas de valores utilizando a técnica 2k fatorial, descrita em detalhes em Law e Kelton (1982), onde cada um dos fatores de impacto foi desdobrado em valores dinâmicos compatíveis com o sistema real no qual o modelo foi baseado. O teste 2k fatorial é especialmente prático no sentido de aferir a aderência do modelo de simulação aos referenciais teóricos, pois permite grande flexibilidade ao analista na formulação de cenários, pois quaisquer combinações possíveis podem ser testadas, dependendo apenas do foco de interesse da pesquisa. Foram realizados também experimentos de simulação com o objetivo de verificar o comportamento do modelo ao atingir estado estacionário. Os testes de validação e os experimentos de simulação apresentaram resultados semelhantes.
Agent based models are considered a milestone in social sciences research mainly because it´s random behavior allows a variety of analysis that were impossible to make due to complexity constraints. The term complexity usually derives from a large number of interacting and dynamic variables which cannot be considered singularly, in order to keep the whole system attributes intact. For that matter, a systemic approach of analysis is required not only to keep this information within the framework of the system, but also to get the emergent properties that come along with the interaction, not observable singling out one or another factor. Considering these very specific characteristics an agent based model, or multiagent system turns to be a more suitable analysis than analytic counterparts due to its very particular capability to deal simultaneously with a large number of random variables. This research work develops an agent based model to study the emergent properties due to public enforcement of criminal law. In order to build a model of criminal behavior suitable to the project, well known theories about crime were taken in consideration, including Shavell and Polinsky (2000), Shavell (2004), Becker (1968), Cohen and Felson(1979) and Clarke (1995). The construct derived from the analysis is a model based on three interacting agents and the environment, namely: citizen-opportunity-state-environment (offender-victim-public enforcement of law-environment). These interacting agents have the ability to define the boundaries of any given offence, regardless the kind, being capable to cope with from a simple misdemeanor (e.g. a traffic violation) to a felony (e.g. a capital murder). Particularly, the citizen-agent behavior has been modeled according to Becker’s (1968) Crime and Punishment seminal work. These remarks explains why originally the term citizen has been used in first place, and not offender. Rational Choice theory says that anyone can be an offender, depending on certain conditions. The State performance regarding its economic impact in terms of the public enforcement of criminal law is seen through the lenses of Shavell and Polinsky’s theory (2000). The opportunity and environment agents’ behavior are mainly based on Cohen and Felson’s Routine Activity Theory (1979) and Clarke’s Rational Choice Theory (1995). The model was implemented using NetLogo 3.1.4 platform (WILENSKY,1999). The simulation program provides a wide range of dynamic options making it very easy to perform any kind of test in order to assess the behavior of a given criminal rule in its dynamic operation. Validation and experimental tests were performed. The resulting responses were very consistent with the theoretical basis on which de model was based on.

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Gil, David. „Modelado y simulación del comportamiento neurológico del tracto urinario inferior: sistema de ayuda al diagnóstico“. Doctoral thesis, Universidad de Alicante, 2008. http://hdl.handle.net/10045/10325.

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Kumar, Akshat. „Exploiting domain structure in multiagent decision-theoretic planning and reasoning“. 2013. https://scholarworks.umass.edu/dissertations/AAI3589066.

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This thesis focuses on decision-theoretic reasoning and planning problems that arise when a group of collaborative agents are tasked to achieve a goal that requires collective effort. The main contribution of this thesis is the development of effective, scalable and quality-bounded computational approaches for multiagent planning and coordination under uncertainty. This is achieved by a synthesis of techniques from multiple areas of artificial intelligence, machine learning and operations research. Empirically, each algorithmic contribution has been tested rigorously on common benchmark problems and, in many cases, real-world applications from machine learning and operations research literature. The first part of the thesis addresses multiagent single-step decision making problems where a single joint-decision is required for the plan. We examine these decision-theoretic problems within the broad frameworks of distributed constraint optimization and Markov random fields. Such models succinctly capture the structure of interaction among different decision variables, which is subsequently exploited by algorithms to enhance scalability. The algorithms presented in this thesis are rigorously grounded on concepts from mathematical programming and optimization. The second part of the thesis addresses multiagent sequential decision making problems under uncertainty and partial observability. We use the decentralized partially observable Markov decision processes (Dec-POMDPs) to formulate multiagent planning problems. To address the challenge of NEXP-Hard complexity and yet push the envelope of scalability, we represent the domain structure in a multiagent system using graphical models such as dynamic Bayesian networks and constraint networks. By exploiting such graphical planning representation in an algorithmic framework composed of techniques from different sub-areas of artificial intelligence, machine learning and operations research, we show impressive gains in increasing the scalability, the range of problems addressed and enabling quality-bounded solutions for multiagent decision theoretic planning. Our contributions for sequential decision making include a) development of efficient dynamic programming algorithms for finite-horizon decision making, resulting in significantly increased scalability w.r.t. the number of agents and multiple orders-of-magnitude speedup over previous best approaches; b) development of probabilistic inference based algorithms for infinite-horizon decision making, resulting in new insights connecting inference techniques from the machine learning literature to multiagent systems; c) development of mathematical programming based scalable techniques for quality bounded solutions in multiagent systems, which has been considered intractable so far. Several of our contributions are some of the first for the respective class of problems. For example, we show for the first time how machine learning is closely related to multiagent decision making via a maximum likelihood formulation of the planning problem. We develop new graphical models and machine learning based inference algorithms for large factored planning problems. We also show for the first time how the problem of optimizing agents' policies can be formulated as a compact mixed-integer program, resulting in optimal solution for a range of Dec-POMDP benchmarks. In summary, we present a synthesis of different techniques from multiple sub-areas of AI, ML and OR to address the scalability and efficiency of algorithms for decision-theoretic reasoning and planning in multiagent systems. Such advances have already shown great promise to bridge the gap between multiagent systems and real-world applications.

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Paquet, Sébastien. „Distributed Decision-Making and TaskCoordination in Dynamic, Uncertain andReal-Time Multiagent Environments“. Phd thesis, 2005. http://tel.archives-ouvertes.fr/tel-00092684.

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La prise de décision dans l'incertain et la coordination sont au coeur des systèmes multiagents. Dans ce type de systèmes, les agents doivent être en mesure de percevoir leur environnement et de prendre des décisions en considérant les autres agents. Lorsque l'environnement est partiellement observable, les agents doivent être en mesure de gérer cette incertitude pour prendre des décisions les plus éclairées possible en considérant les informations incomplètes qu'ils ont pu acquérir. Par ailleurs, dans le contexte d'environnements multiagents coopératifs, les agents doivent être en mesure de coordonner leurs actions de manière à pouvoir accomplir des tâches demandant la collaboration de plus d'un agent. Dans cette thèse, nous considérons des environnements multiagents coopératifs complexes (dynamiques, incertains et temps-réel). Pour ce type d'environnements, nous proposons une approche de prise de décision dans l'incertain permettant une coordination flexible entre les agents. Plus précisément, nous présentons un algorithme de résolution en ligne de processus de décision de Markov partiellement observables (POMDPs). Par ailleurs, dans de tels environnements, les tâches que doivent accomplir les agents peuvent devenir très complexes. Dans ce cadre, il peut devenir difficile pour les agents de déterminer le nombre de ressources nécessaires à l'accomplissement de chacune des tâches. Pour résoudre ce problème, nous proposons donc un algorithme d'apprentissage permettant d'apprendre le nombre de ressources nécessaires à l'accomplissement des tâches selon les caractéristiques de celles-ci. Dans un même ordre d'idée, nous proposons aussi une méthode d'ordonnancement permettant d'ordonner les différentes tâches des agents de manière à maximiser le nombre de tâches pouvant être accomplies dans un temps limité. Toutes ces approches ont pour but de permettre la coordination d'agents pour l'accomplissement efficace de tâches complexes dans un environnement multiagent partiellement observable, dynamique et incertain. Toutes ces approches ont démontré leur efficacité lors de tests effectués dans l'environnement de simulation de la RoboCup- Rescue.

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Carlin, Alan Scott. „Decision-Theoretic Meta-reasoning in Partially Observable and Decentralized Settings“. 2012. https://scholarworks.umass.edu/open_access_dissertations/508.

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This thesis examines decentralized meta-reasoning. For a single agent or multiple agents, it may not be enough for agents to compute correct decisions if they do not do so in a timely or resource efficient fashion. The utility of agent decisions typically increases with decision quality, but decreases with computation time. The reasoning about one's computation process is referred to as meta-reasoning. Aspects of meta-reasoning considered in this thesis include the reasoning about how to allocate computational resources, including when to stop one type of computation and begin another, and when to stop all computation and report an answer. Given a computational model, this translates into computing how to schedule the basic computations that solve a problem. This thesis constructs meta-reasoning strategies for the purposes of monitoring and control in multi-agent settings, specifically settings that can be modeled by the Decentralized Partially Observable Markov Decision Process (Dec-POMDP). It uses decision theory to optimize computation for efficiency in time and space in communicative and non-communicative decentralized settings. Whereas base-level reasoning describes the optimization of actual agent behaviors, the meta-reasoning strategies produced by this thesis dynamically optimize the computational resources which lead to the selection of base-level behaviors.

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Finnson, John. „Modeling Trust in Multiagent Mobile Vehicular Ad-Hoc Networks through Enhanced Knowledge Exchange for Effective Travel Decision Making“. Thesis, 2012. http://hdl.handle.net/10012/6637.

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This thesis explores how to effectively model trust in the environment of mobile vehicular ad-hoc networks. We consider each vehicle’s travel path planning to be guided by an intelligent agent that receives traffic reports from other agents in the environment. Determining the trustworthiness of these reports is thus a critical task. We take as a starting point the multi-dimensional trust model of Minhas et al. That work had a two-phased approach: i) model trust and ii) execute an algorithm for using that trust modeling, when deciding what route to take. The framework presented in this thesis aims to clarify i) the messaging that should be supported, ii) the internal representation of the messaging and the trust information and iii) the algorithms for sending and receiving information (thus updating knowledge) in order to perform decision making during route planning. A significant contribution is therefore offered through clarification and extension of the original trust modeling approach. In addition we design a comprehensive, extensive simulation testbed that is used to validate the effectiveness and robustness of the model. This testbed supports a variety of metrics and is able to perform testing in environments with a large number of cars. This constitutes the second significant contribution of the thesis. Overall, we present a valuable model for knowledge management in mobile vehicular ad-hoc networks through a combination of trust modeling, ontological representation of concepts and facts, and a methodology for discovering and updating user models. Included is a representation and implementation of both a push-based and pull-based messaging protocol. We also demonstrate the effectiveness of this model through validation conducted using our simulation testbed, focusing first on a subset of the multi-faceted trust model in order to highlight the value of the underlying representation, decision making algorithm and simulation metrics. One very valuable result is a demonstration of the importance of the combined use of the different dimensions employed in the trust modeling.

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Amato, Christopher. „Increasing scalability in algorithms for centralized and decentralized partially observable Markov decision processes: Efficient decision-making and coordination in uncertain environments“. 2010. https://scholarworks.umass.edu/dissertations/AAI3427492.

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As agents are built for ever more complex environments, methods that consider the uncertainty in the system have strong advantages. This uncertainty is common in domains such as robot navigation, medical diagnosis and treatment, inventory management, sensor networks and e-commerce. When a single decision maker is present, the partially observable Markov decision process (POMDP) model is a popular and powerful choice. When choices are made in a decentralized manner by a set of decision makers, the problem can be modeled as a decentralized partially observable Markov decision process (DEC-POMDP). While POMDPs and DEC-POMDPs offer rich frameworks for sequential decision making under uncertainty, the computational complexity of each model presents an important research challenge. As a way to address this high complexity, this thesis develops several solution methods based on utilizing domain structure, memory-bounded representations and sampling. These approaches address some of the major bottlenecks for decision-making in real-world uncertain systems. The methods include a more efficient optimal algorithm for DEC-POMDPs as well as scalable approximate algorithms for POMDPs and DEC-POMDPs. Key contributions include optimizing compact representations as well as automatic structure extraction and exploitation. These approaches increase the scalability of algorithms, while also increasing their solution quality.

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Jung, Hyunggu. „Reasoning about Benefits and Costs of Interaction with Users in Real-time Decision Making Environments with Application to Healthcare Scenarios“. Thesis, 2010. http://hdl.handle.net/10012/5337.

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This thesis examines the problem of having an intelligent agent reasoning about interaction with users in real-time decision making environments. Our work is motivated by the models of Fleming and Cheng, which reason about interaction sensitive to both expected quality of decision (following interaction) and cost of bothering users. In particular, we are interested in dynamic, time critical scenarios. This leads first of all to a novel process known as strategy regeneration, whereby the parameter values representing the users and the task at hand are refreshed periodically, in order to make effective decisions about which users to interact with, for the best decision making. We also introduce two new parameters that are modeled: each user's lack of expertise (with the task at hand) and the level of criticality of each task. These factors are then integrated into the process of reasoning about interaction to choose the best overall strategy, deciding which users to ask to resolve the current task. We illustrate the value of our framework for the application of decision making in hospital emergency room scenarios and offer validation of the approach, both through examples and from simulations. To sum up, we provide a framework for reasoning about interaction with users through user modeling for dynamic environments. In addition, we present some insights into how to improve the process of hospital emergency room decision making.

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Haider, Kamal. „Multi-agent decision support system in avionics : improving maintenance and reliability predictions in an intelligent environment“. 2009. http://arrow.unisa.edu.au:8081/1959.8/93158.

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Safety of the airborne platforms rests heavily on the way they are maintained. This maintenance includes repairs and testing, to reduce platform down time. Maintenance is performed using generic and specific test equipment within the existing maintenance management system (MMS). This thesis reports the work undertaken to improve maintainability and availability of avionics systems using an intelligent decision support system (IDSS). In order to understand the shortcomings of the existing system, the prevalent practices and methodologies are researched. This research thesis reports the development and implementation of an IDSS and the significant improvements made by this IDSS by integrating autonomous and independent information sources by employing a multi-agent system (MAS). Data mining techniques and intelligence agents (IA) are employed to create an expert system. The developed IDSS successfully demonstrates its ability to integrate and collate the available information and convert into valuable knowledge. Using this knowledge, the IDSS is able to generate interpreted alerts, warnings and recommendations thereby reasonably improving platform maintainability and availability. All facets of integrated logistics support (ILS) are considered to create a holistic picture. As the system ages, the IDSS also matures to assist managers and maintainers in making informed decisions about the platform, the unit under test (UUT) and even the environment that supports the platform.

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Ramos, Nélio Duarte Pereira. „Bases para construir modelos de conhecimento coletivo no paradigma simbólico“. Master's thesis, 2016. http://hdl.handle.net/10400.13/1584.

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Na idade da informação a capacidade de recolher, escolher e armazenar a informação relevante é um propósito tão nobre como complexo. A computação ubíqua permite que o conhecimento esteja distribuído por diversos locais e intervenientes. Na Inteligência Artificial o conceito de Agente Inteligente faz já parte do léxico comum e a sua utilidade tem vindo a ser demonstrada para resolução de diversos problemas. Para tal contribuiu a sua capacidade de sistematizar o processo de trabalhar a informação. A interligação entre estes agentes cria um conhecimento comum a todo o grupo. Neste trabalho são propostas bases que permitem a construção de um Modelo de conhecimento coletivo. Estas bases permitem uma visão para a representação do conhecimento num grupo de agentes com recurso a ferramentas do paradigma simbólico e através da revisão de crenças e argumentação para a dinâmica de conhecimento entre os elementos de um grupo.
In the information age the ability to collect, select and store relevant information is a purpose as noble as complex. Ubiquitous computing enables knowledge distributed on various locations and actors. In Artificial Intelligence the concept of Intelligent Agent is already part of the common lexicon and its usefulness has been demonstrated to solve various problems. Contributing to this is the ability to systematize the process of information manipulating. The interconnection between these agents creates a common knowledge of the whole group. This work proposal a basis to allow the construction of a collective knowledge model. These allows for a vision of knowledge representation in an agent group using the symbolic paradigm tools and through the belief revision and argumentation for knowledge dynamic between the elements of the group.

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Dissertationen zum Thema „Multiagent decision“

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