Bibliografias temáticas / Multiagent decision

Literatura científica selecionada sobre o tema "Multiagent decision"

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Publicado: 25 de maio de 2024

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Artigos de revistas sobre o assunto "Multiagent decision"

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Kumar, Akshat, Shlomo Zilberstein e Marc Toussaint. "Probabilistic Inference Techniques for Scalable Multiagent Decision Making". Journal of Artificial Intelligence Research 53 (29 de junho de 2015): 223–70. http://dx.doi.org/10.1613/jair.4649.

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Decentralized POMDPs provide an expressive framework for multiagent sequential decision making. However, the complexity of these models---NEXP-Complete even for two agents---has limited their scalability. We present a promising new class of approximation algorithms by developing novel connections between multiagent planning and machine learning. We show how the multiagent planning problem can be reformulated as inference in a mixture of dynamic Bayesian networks (DBNs). This planning-as-inference approach paves the way for the application of efficient inference techniques in DBNs to multiagent decision making. To further improve scalability, we identify certain conditions that are sufficient to extend the approach to multiagent systems with dozens of agents. Specifically, we show that the necessary inference within the expectation-maximization framework can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We further show that a number of existing multiagent planning models satisfy these conditions. Experiments on large planning benchmarks confirm the benefits of our approach in terms of runtime and scalability with respect to existing techniques.
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Han, Xiaoyu. "Application of Reinforcement Learning in Multiagent Intelligent Decision-Making". Computational Intelligence and Neuroscience 2022 (16 de setembro de 2022): 1–6. http://dx.doi.org/10.1155/2022/8683616.

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The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment.
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Narayanan, Lakshmi Kanthan, Suresh Sankaranarayanan, Joel J. P. C. Rodrigues e Pascal Lorenz. "Multi-Agent-Based Modeling for Underground Pipe Health and Water Quality Monitoring for Supplying Quality Water". International Journal of Intelligent Information Technologies 16, n.º 3 (julho de 2020): 52–79. http://dx.doi.org/10.4018/ijiit.2020070103.

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This article discusses distributed monitoring through the deployment of various multiagents in the IoT-Fog-based water distribution network (WDN). This will ensure the right amount of water supplied with respect to demand forecasted to residents. In addition, underground pipe health is also monitored by means of a multiagent based on hydraulic parameters supplying water forecasted with minimal losses which would minimize the operational and material cost involved in recovery or repair. Lastly, there are agents deployed towards leakage monitoring and anti-theft detection of water. The multiagents act upon various parameters of hydrology and analysis is based on the data acquired by the various sensors deployed in the water distribution network which perform partial automation of the disconnection of the supply during extreme critical conditions.
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Xiang, Yang, e Frank Hanshar. "Multiagent Decision Making in Collaborative Decision Networks by Utility Cluster Based Partial Evaluation". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, n.º 02 (abril de 2015): 149–91. http://dx.doi.org/10.1142/s0218488515500075.

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We consider optimal multiagent cooperative decision making in stochastic environments. The focus is on simultaneous decision making, during which agents cooperate by limited communication. We model the multiagent system as a collaborative decision network (CDN). Several techniques are developed to improve efficiency for decision making with CDNs. We present an equivalent transformation of CDN subnets to facilitate model manipulation. We propose partial evaluation to allow action profiles evaluated with reduced computation. We decompose a CDN subnet, based on clustering of utility variables. A general simultaneous decision making algorithm suite is developed that embeds these techniques. We show that the new algorithm suite improves efficiency by a combination of a linear factor and an exponential factor.
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XIANG, YANG, e FRANK HANSHAR. "MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, n.º 06 (dezembro de 2011): 939–76. http://dx.doi.org/10.1142/s0218488511007416.

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We investigate a class of multiagent planning problems termed multiagent expedition, where agents move around an open, unknown, partially observable, stochastic, and physical environment, in pursuit of multiple and alternative goals of different utility. Optimal planning in multiagent expedition is highly intractable. We introduce the notion of conditional optimality, decompose the task into a set of semi-independent optimization subtasks, and apply a decision-theoretic multiagent graphical model to solve each subtask optimally. A set of techniques are proposed to enhance modeling so that the resultant graphical model can be practically evaluated. Effectiveness of the framework and its scalability are demonstrated through experiments. Multiagent expedition can be characterized as decentralized partially observable Markov decision processes (Dec-POMDPs). Hence, this work contributes towards practical planning in Dec-POMDPs.
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Nunes, Ernesto, Julio Godoy e Maria Gini. "Multiagent Decision Making on Transportation Networks". Journal of Information Processing 22, n.º 2 (2014): 307–18. http://dx.doi.org/10.2197/ipsjjip.22.307.

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Maturo, Antonio, e Aldo G. S. Ventre. "Reaching consensus in multiagent decision making". International Journal of Intelligent Systems 25, n.º 3 (março de 2010): 266–73. http://dx.doi.org/10.1002/int.20401.

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He, Liu, Haoning Xi, Tangyi Guo e Kun Tang. "A Generalized Dynamic Potential Energy Model for Multiagent Path Planning". Journal of Advanced Transportation 2020 (24 de julho de 2020): 1–14. http://dx.doi.org/10.1155/2020/1360491.

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Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.
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Xu, Yang, Xiang Li e Ming Liu. "Modeling and Simulation of Complex Network Attributes on Coordinating Large Multiagent System". Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/412479.

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With the expansion of distributed multiagent systems, traditional coordination strategy becomes a severe bottleneck when the system scales up to hundreds of agents. The key challenge is that in typical large multiagent systems, sparsely distributed agents can only communicate directly with very few others and the network is typically modeled as an adaptive complex network. In this paper, we present simulation testbedCoordSimbuilt to model the coordination of network centric multiagent systems. Based on the token-based strategy, the coordination can be built as a communication decision problem that agents make decisions to target communications and pass them over to the capable agents who will potentially benefit the team most. We have theoretically analyzed that the characters of complex network make a significant difference with both random and intelligent coordination strategies, which may contribute to future multiagent algorithm design.
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Szymak, Piotr. "Comparison of Centralized, Dispersed and Hybrid Multiagent Control Systems of Underwater Vehicles Team". Solid State Phenomena 180 (novembro de 2011): 114–21. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.114.

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Multiagent systems controlling robots can have different structures, depending on a way of generating decision in these systems. Decisions can be work out in centralized, decentralized or even hybrid way (hybrid system is a connection of both centralized and decentralized systems). In the case of controlling a team of underwater vehicles, it is significant to examine different structures of multiagent systems for choosing the best one for defined underwater task. In the paper, results of operation of three different structures (centralized, dispersed - decentralized and hybrid) of multiagent control systems of underwater vehicles team were presented. The systems were tested in predator-prey problem. In this problem, a team of three underwater vehicles had to catch another underwater robot escaping with larger velocity.
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Mais fontes

Teses / dissertações sobre o assunto "Multiagent decision"

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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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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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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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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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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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Warden, Tobias [Verfasser], Otthein [Akademischer Betreuer] Herzog, Otthein [Gutachter] Herzog e 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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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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Livros sobre o assunto "Multiagent decision"

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Ventre, Aldo G. S., Antonio Maturo, Šárka Hošková-Mayerová e Janusz Kacprzyk, eds. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35635-3.

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Ventre, Aldo G. S. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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3

Anders, Rantzer, e SpringerLink (Online service), eds. Distributed Decision Making and Control. London: Springer London, 2012.

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4

Jane, Doan, ed. Choosing to learn: Ownership and responsibility in a primary multiage classroom. Portsmouth, NH: Heinemann, 1996.

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5

Probabilistic Reasoning in Multiagent Systems. Cambridge University Press, 2002.

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6

Maturo, Antonio, Aldo G. S. Ventre e Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.

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7

Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.

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8

Kacprzyk, Janusz, Antonio Maturo, Aldo G. S. Ventre e Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2015.

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9

Xiang, Yang. Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2010.

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10

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2002.

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Capítulos de livros sobre o assunto "Multiagent decision"

1

Schröter, Kay, e Diemo Urbig. "C-IPS: Specifying Decision Interdependencies in Negotiations". In Multiagent System Technologies, 114–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30082-3_9.

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Xiang, Yang, e Frank Hanshar. "Multiagent Decision by Partial Evaluation". In Advances in Artificial Intelligence, 242–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30353-1_21.

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Brandt, Felix. "Tournament Solutions and Their Applications to Multiagent Decision Making". In Multiagent System Technologies, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16178-0_1.

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Richter, Jan, Matthias Klusch e Ryszard Kowalczyk. "Monotonic Mixing of Decision Strategies for Agent-Based Bargaining". In Multiagent System Technologies, 113–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24603-6_12.

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López, Beatriz, Carles Pous, Pablo Gay e Albert Pla. "Multi Criteria Decision Methods for Coordinating Case-Based Agents". In Multiagent System Technologies, 54–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_6.

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Schwaiger, Arndt, e Björn Stahmer. "SimMarket: Multiagent-Based Customer Simulation and Decision Support for Category Management". In Multiagent System Technologies, 74–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39869-1_7.

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Ng, Zhan Sheng, Aaron Yu Siang Tan, Arief Adhitya e Rajagopalan Srinivasan. "Agent-Based Model for Decision Support in Multi-Site Manufacturing Enterprises". In Multiagent System Technologies, 103–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_10.

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Guttmann, Christian. "Towards a Taxonomy of Decision Making Problems in Multi-Agent Systems". In Multiagent System Technologies, 195–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_19.

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Widmer, Tobias, e Marc Premm. "Agent-Based Decision Support for Allocating Caregiving Resources in a Dementia Scenario". In Multiagent System Technologies, 233–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27343-3_13.

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Güneş, Taha D., Timothy J. Norman e Long Tran-Thanh. "Budget Limited Trust-Aware Decision Making". In Autonomous Agents and Multiagent Systems, 101–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71679-4_7.

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Trabalhos de conferências sobre o assunto "Multiagent decision"

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Campbell, Trevor, Luke Johnson e Jonathan P. How. "Multiagent allocation of Markov decision process tasks". In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580186.

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Xuan, Ping. "Modeling plan coordination in multiagent decision processes". In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329396.

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Rigopoulos, G., J. Psarras e N. V. Karadimas. "A Multiagent Model For Group Decision Support". In 21st Conference on Modelling and Simulation. ECMS, 2007. http://dx.doi.org/10.7148/2007-0096.

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Yucelen, Tansel, e John Daniel Peterson. "Active-passive networked multiagent systems". In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040479.

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Kumar, Akshat. "Multiagent Decision Making and Learning in Urban Environments". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/895.

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Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents---from self-driving cars, ships to aerial drones---that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent systems.
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Czibula, Gabriela, Adriana Mihaela Guran, Grigoreta Sofia Cojocar e Istvan Gergely Czibula. "Multiagent Decision Support Systems based on Supervised Learning". In 2008 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2008). IEEE, 2008. http://dx.doi.org/10.1109/aqtr.2008.4588943.

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Coleman, Norman, Ching-Fang Lin, Jianhua Ge e Sarah Braasch. "Intelligent multiagent modeling and decision system for battlefield". In Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-3992.

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Tounsi, Jihene, Julien Boissiere e Georges Habchi. "Multiagent Decision Making For SME Supply Chain Simulation". In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0210.

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Tounsi, Jihene, Julien Boissiere e Georges Habchi. "Multiagent Decision Making For SME Supply Chain Simulation". In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0211.

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Uzhva, Denis, Oleg Granichin e Olga Granichina. "Compressed Cluster Sensing in Multiagent IoT Control". In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992703.

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Relatórios de organizações sobre o assunto "Multiagent decision"

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David, Gabrielle, D. Somerville, Julia McCarthy, Spencer MacNeil, Faith Fitzpatrick, Ryan Evans e David Wilson. Technical guide for the development, evaluation, and modification of stream assessment methods for the Corps Regulatory Program. Engineer Research and Development Center (U.S.), outubro de 2021. http://dx.doi.org/10.21079/11681/42182.

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The U.S. Army Corps Regulatory Program considers the loss (impacts) and gain (compensatory mitigation) of aquatic resource functions as part of Clean Water Act Section 404 permitting and compensatory mitigation decisions. To better inform this regulatory decision-making, the Regulatory Program needs transparent and objective approaches to assess the function and condition of aquatic resources, including streams. Therefore, the Regulatory Program needs function-based stream assessments (1) to characterize a stream’s condition or function, (2) to improve understanding of the impact of a proposed action on an aquatic resource, and/or (3) to inform the development of stream compensatory mitigation tools rooted in stream condition and/or function. A function-based stream assessment can provide regulatory decision makers with the resources to objectively consider alternatives, minimize impacts, assess unavoidable impacts, determine mitigation requirements, and monitor the success of mitigation projects. A multiagency National Committee on Stream Assessment (NCSA) convened to create these guidelines to inform the development of new methods and evaluation of both national-level and regional methods currently in use. The resulting guidelines present nine phases, including rationale and recommendations to facilitate work efforts. The NCSA hopes that this technical guide promotes transparency, technical defensibility, and consistent application of stream assessments in the Regulatory Program.
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