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Relevant bibliographies by topics / Multi-agent paradigms

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Academic literature on the topic 'Multi-agent paradigms'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Multi-agent paradigms"

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Patel, Rakesh B., and Satish K. Shah. "Multi-agent controller using soft computational paradigms." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 138–41. http://dx.doi.org/10.15373/2249555x/feb2013/48.

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HORLING, BRYAN, and VICTOR LESSER. "A survey of multi-agent organizational paradigms." Knowledge Engineering Review 19, no. 4 (December 2004): 281–316. http://dx.doi.org/10.1017/s0269888905000317.

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Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and disadvantages, and provide examples of how they may be instantiated and maintained. This summary will facilitate the comparative evaluation of organizational styles, allowing designers to first recognize the spectrum of possibilities, and then guiding the selection of an appropriate organizational design for a particular domain and environment.

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Baykasoglu, Adil, Vahit Kaplanoglu, Rizvan Erol, and Cenk Sahin. "A MULTI-AGENT FRAMEWORK FOR LOAD CONSOLIDATION IN LOGISTICS." TRANSPORT 26, no. 3 (October 5, 2011): 320–28. http://dx.doi.org/10.3846/16484142.2011.622141.

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Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of third-party logistics companies.

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Surynek, Pavel. "Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12409–17. http://dx.doi.org/10.1609/aaai.v35i14.17472.

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We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MG-MAPF: a heuristic search-based algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the satisfiability modulo theories (SMT), called SMT-Hamiltonian-CBS (SMT-HCBS).

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DARBYSHIRE, PAUL. "EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT." Advances in Complex Systems 06, no. 03 (September 2003): 405–26. http://dx.doi.org/10.1142/s0219525903000979.

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Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of learning is not sufficient for agents to adapt to this hostile environment. However, by allowing the agents to communicate their respective rewards for actions performed as the simulation progresses, the rate of learning can be increased sufficiently to significantly increase the teams chances of survival. This paper presents the results of trials to measure the success of a team-based approach to the reinforcement-learning problem in a distillation, using reward communication to increase learning rates.

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Tokadlı, Güliz, Kaitlyn Ouverson, Chase Meusel, Austin Garcia, Stephen B. Gilbert, and Michael C. Dorneich. "An Analysis of Video Games Using the Dimensions of Human-Agent Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 716–20. http://dx.doi.org/10.1177/1541931218621163.

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Designers of human-agent interaction techniques may benefit from an analysis of existing video games that include aspects of human-agent teaming. Many popular multi-player video games have been designed to integrate multiple human and computer agents in pursuit of a common objective and can serve as a testbed to explore novel interaction methods in human-agent teams. A guiding framework of human-agent interaction was created to bridge best practices between video game and real-world domains. The framework was used to analyze games on five main dimensions: 1) Levels of Automation, 2) Levels of Interaction, 3) Control Mode, 4) Teaming, and 5) Interaction Timing. Two video games, Final Fantasy XIV and Mass Effect, were assessed to identify human-agent interaction paradigms, and ramifications for real-world applications for human-agent teaming. This research draws on interaction design principles, human-agent interaction theory, and existing video games to offer human-agent team designers potential examples of successful interaction paradigms.

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DE GRANDE, PABLO, and MANUEL EGUIA. "STRATEGY SPACE EXPLORATION OF A MULTI-AGENT MODEL FOR THE LABOR MARKET." International Journal of Modern Physics C 17, no. 03 (March 2006): 357–71. http://dx.doi.org/10.1142/s0129183106008534.

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We present a multi-agent system where typical labor market mechanisms emerge. Based on a few simple rules, our model allows for different interpretative paradigms to be represented and for different scenarios to be tried out. We thoroughly explore the space of possible strategies both for those unemployed and for companies and analyze the trade-off between these strategies regarding global social and economical indicators.

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Dosi, G., M. C. Pereira, A. Roventini, and M. E. Virgillito. "Technological paradigms, labour creation and destruction in a multi-sector agent-based model." Research Policy 51, no. 10 (December 2022): 104565. http://dx.doi.org/10.1016/j.respol.2022.104565.

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Ygge, F., and H. Akkermans. "Decentralized Markets versus Central Control: A Comparative Study." Journal of Artificial Intelligence Research 11 (October 1, 1999): 301–33. http://dx.doi.org/10.1613/jair.627.

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Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. In order to validate such claims that are repeatedly made in software agent publications, empirical in-depth studies of advantages and weaknesses of multi-agent solutions versus conventional ones in practical applications are needed. Climate control in large buildings is one application area where multi-agent systems, and market-oriented programming in particular, have been reported to be very successful, although central control solutions are still the standard practice. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This article gives a detailed analysis and comparison, so as to learn about differences between standard versus agent approaches, and yielding new insights about benefits and limitations of computational markets. An important outcome is that ``local information plus market communication produces global control''.

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Yang, Shuo, Xinjun Mao, Sen Yang, and Zhe Liu. "Towards a hybrid software architecture and multi-agent approach for autonomous robot software." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141771608. http://dx.doi.org/10.1177/1729881417716088.

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To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

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Dissertations / Theses on the topic "Multi-agent paradigms"

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Moraïtis, Pavlos. "Paradigme multi-agent et prise de décision distribuée." Paris 9, 1994. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1994PA090046.

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Pawloski, Joel S. "Modeling tactical level combat using a Multi-agent System Design Paradigm (GI Agent)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA391678.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2001.
Thesis advisors, Michael Zyda, John E. Hiles. Includes bibliographical references (p. 81-82). Also available online.

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Alqithami, Saad. "Network Organization Paradigm." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1293.

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In a complex adaptive system, diverse agents perform various actions without adherence to a predefined structure. The achievement of collaborative actions will be the result of continual interactions among them that shape a dynamic network. Agents may form an ad hoc organization based on the dynamic network of interactions for the purpose of achieving a long-term objective, which we termed a Network Organization (NO). Fervent and agile communication on social networking sites provides opportunities for potential issues to trigger individuals into individual actions as well as the attraction and mobilization of like-minded individuals into an NO that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring proliferation and the diffusion rate of the Occupy Movement. We are motivated by a spontaneously formed NO as well as the quality of plasticity that enables the organization to change rapidly to describe an NO. Thus, we present a paradigm that serves as a reference model for organizations of socially networked individuals. This paradigm suggests modular components that can be combined to form an ad hoc network organization of agents. We touch on how this model accounts for external change in an environment through internal adjustment. For the predominant influences of the network substrate in an NO, multiple effects of it have an impact on the NO behaviors and directions. We envisioned several dimensions of such effects to include synergy, social capital, externality, influence, etc. A special focus in this work is measuring synergy and social capital as two predominant network effects. Synergy is perceived as different modalities of compatibility among agents when performing a set of coherent and correspondingly different actions. When agents are under no structural obligation to contribute, synergy is quantified through multiple forms of serendipitous agent chosen benevolence among them. The approach is to measure four types of benevolence and the pursuant synergies stemming from agent interactions. Social capital is another effect of networking that describes the accumulation of positive values of social flow and perceived trust plus abundance of communication over the common topic of NO. We provide measurement of social capital based on an agents’ expected benevolence. We examine those two effects in two different case studies — one case of a virtual organization and another of a real world terrorist organization — that best illustrate the main tenets of our conceptualization.

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Roddy, Kimberly A. Dickson Michael R. "Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://edocs.nps.edu/npspubs/scholarly/theses/2000/Sep/00Sep_Roddy.pdf.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, September 2000.
"September 2000." Thesis advisor(s): Michael Zyda, John Hiles. CD-ROM titled: RELATE: A relation-centric MAS simulation development package. Includes bibliographical references (p. 133-137). Also available online.

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Roddy, Kimberly A., and Michael R. Dickson. "Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm." Thesis, Monterey, California. Naval Postgraduate School, 2000. http://hdl.handle.net/10945/7739.

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Today's modeling and simulation communities are being challenged to create rich, detailed models incorporating human decision-making and organizational behavior. Recent advances in distributed artificial intelligence and complex systems theory have demonstrated that such ill-defined problems can be effectively modeled with agent-based simulation techniques using multiple, autonomoous, adaptive entities. RELATE, a relation-centric design paradigm for multi-agent systems (MAS), is presented to assist developers incorporate MAS solutions into their simulations. RELATe focuses the designer on six key concepts of MAS simulations: relationships, environment, laws, agents, things, and effectors. A library of Java classes is presented which enables the user to rapidly prototype an agent-based simulation. This library utilizes the Java programming language to support cross-platform and web based designs. All Java classes and interfaces are fully documented using HTML Javadoc format. Two reference cases are provided that allow for easy code reuse and modification. Finally, an existing metworked DIS-Java-VRML simulation was modified to demonstrate the ability to utilize the RELATE library to add agents to existing applications. LCDR Kim Roddy focused on the development and refinement of the RELATE design paradigm, while LT Mike Dickson focused on the actual Java implementation. Joint work was conducted on all research and reference cases

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Dinc, Mustafa. "Modeling Bluetooth radio technology simulation using Multi-agent based system and Genetic Algorithm design paradigm." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA391713.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2001.
Thesis advisors, John E. Hiles, Michael Zyda. Includes bibliographical references (p. 107-109). Also Available online.

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Diot, Nicolas. "SAMP : Plateforme de modélisation à partir du paradigme multi-agents pour l’univers du jeu vidéo : vers un développement accessible et une gestion adaptée des interactions." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD065/document.

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En quelques années, les domaines des jeux vidéo et des systèmes multi-agents (SMA) ont pris de plus en plus de places dans la vie de chacun. Malgré des similitudes assez fortes (présences d’entité dans les vidéo pouvant être assimilées à des agents), les SMA ne sont presque jamais utilisés dans le développement de jeux. Ce mémoire présente Shine Agent Modeling Platform (SAMP), une plateforme visant intégrer le paradigme multi-agents au sein du développement de jeux vidéo. Cette fusion permet l’utilisation de la puissance des multi-agents au sein des jeux vidéo.SAMP propose une approche au niveau des interactions permettant de réduire le coût de traitement de ces interactions en optimisant le nombre de recherches effectuées dans l’environnement.En plus d’intégrer le paradigme multi-agents, SAMP vise à être accessible à un maximum d’utilisateurs en proposant une interface de modélisation entièrement graphique. Un système d’importation de modèles comportementaux permet de créer deuxniveaux de modélisation : un niveau proche de la logique développement informatique et un niveau proche de la logique métier de l’utilisateur.SAMP est intégré à un moteur de jeux vidéo, Shine Engine, permettant de générer les environnements graphiques dans lesquels les agents évolueront
In recent years, video games domains and multiagents systems (MAS) domains took more and more place into the life of many pepole. Despite of strong similarities (video games entities wich can be assimilated to agents), MAS are very rarely used during the development of video games. This submission presents the Shine Agent ModelingPlatform (SAMP), a framework trying to integrate the multi-agents paradigm within the development of video games. The purpose is to integrate the efficiency of the MAS within the video games.SAMP provides an approach to enhance the interactions between agents. This approach reduces the number of searches within the environment. In addition to integrate the multi-agents paradigm within the video games, SAMP aims to be userfriendly by proposing a full graphical interface to MAS. An import/export system of these modelsallows users to create two modeling levels: one close to the computer sciences logic and the second close the business logic of the user.SAMP is integrated in a video games engine: Shine Engine. This integration allows to generate the graphic environment in which agents will live

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Rasouli, Disfani Vahid. "Optimization and Control for Microgrid and Power Electronic Converters." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5764.

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The proposed dissertation research investigates Optimization and Control for Microgrid and Power Electronic Converters. The research has two major parts: i- Microgrid Operation and Control, ii- Power Electronic Converter Control and Optimization. In the first part, three focuses are investigated. First, a completely distributed algorithm is developed for dc optimal power flow problem for power distribution systems as one of the necessary functions considered in unit-commitment problem in day-ahead markets. This method is derived based upon the partial primal-dual representation of the economic dispatch problem, which is finally translated to DC-OPF problem. Second, the optimal interaction between the utility and communities will be studied, due to its improtance in real-time markets. The objective of this section will be to develop an iterative agent-based algorithm for optimal utility-community control. The algorithm will consider the AC power system constraints to maintain power system stability. In this algorithm, a simplified model of microgrid is considered. In the third focus, a comprehensive model of microgrid is taken into account. The optimal operation of the microgrid considering energy storage systems and renewable energy resources is investigated. The interaction of such microgrids with the main grid to define the optimal operation of the entire embedded system is studied through two iterative methods. In the microgrid's internal problem, a moving-horizon algorithm is considered to define the optimal dispatch of all distributed energy resources while considering the time-correlated constraints of energy storage systems. A thorough analysis of the effects of the size of storage systems on energy and reserve market parameters are also performed. In the second part, the focus of research is to develop optimal control strategies for Power Electronic Converters. A Model Predictive Control (MPC) switching method is proposed for Modular Multilevel Converters (MMC). The optimal solution of MPC problem is then represented as an optimization problem. Due to lack of efficient algorithms to seek the optimal solution, a fast algorithm will be proposed in this research. The method proposed reduces the number of possible solutions and computation efforts dramatically.

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Bevilacqua, Sara. "New biologically inspired models towards understanding the Italian Power Exchange market." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15036/.

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Questa tesi tratta l’estensione di un modello ad agenti per la simulazione e lo studio del mercato elettrico Italiano. Nel modello, gli agenti, che rappresentano le compagnie energetiche presenti nel mercato Italiano, competono tra loro con l’obiettivo di avere il più alto profitto. Ogni agente determina la propria strategia attraverso un algoritmo genetico, che opera su una popolazione di strategie. Questo modello presenta però due limitazioni: (1) agli impianti di produzione di un agente, situati nella stessa zona geografica e aventi in comune la tecnologia di produzione, viene applicata la stessa strategia; (2) per la corretta evoluzione delle popolazioni private, gli agenti si scambiano informazioni circa la loro scelta strategica. Con questa tesi vogliamo dimostrare, prima di tutto, che il rilassamento delle ipotesi del primo punto non peggiora la qualità dei risultati. Successivamente, vogliamo testare l’efficacia dell’algoritmo genetico, applicando e testando algoritmi alternativi, quali l’ottimizzazione Monte Carlo e l’algoritmo Particle Swarm Optimization. La seconda parte del lavoro si focalizza sull’introduzione di agenti intelligenti che siano capaci di aggiornare due popolazioni, una composta dalle strategie dei concorrenti e l’altra con le proprie strategie. In questo modo, gli agenti non devono più condividere le proprie scelte strategiche, ma cercano di fare previsioni sui concorrenti, come nel mondo reale, e reagire di conseguenza, avvalendosi di tecniche di adversarial reasoning. Vogliamo infine mostrare i risultati che si ottengono con questa ultima estensione.

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Das, Kaushik. "Multi-Agent Positional Consensus Under Various Information Paradigms." Thesis, 2013. http://hdl.handle.net/2005/2604.

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This thesis addresses the problem of positional consensus of multi-agent systems. A positional consensus is achieved when the agents converge to a point. Some applications of this class of problem is in mid-air refueling of the aircraft or UAVs, targeting a geographical location, etc. In this research work some positional consensus algorithms have been developed. They can be categorized in two part (i) Broadcast control based algorithm (ii) Distributed control based algorithm. In case of broadcast based algorithm control strategies for a group of agents is developed to achieve positional consensus. The problem is constrained by the requirement that every agent must be given the same control input through a broadcast communication mechanism. Although the control command is computed using state information in a global framework, the control input is implemented by the agents in a local coordinate frame. The mathematical formulation has been done in a linear programming framework that is computationally less intensive than earlier proposed methods. Moreover, a random perturbation input in the control command, that helps to achieve reasonable proximity among agents even for a large number of agents, which was not possible with the existing strategy in the literature, is introduced. This method is extended to achieve positional consensus at a pre-specified location. A comparison between the LP approach and the existing SOCP based approach is also presented. Some of the algorithm has been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots. In the second case of broadcast based algorithm, a decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus has been developed using the broadcast concept. Even here, the mathematical formulation has done using a linear programming framework. Each agent has some sensing radius and it is capable of sensing position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. In case of distributed algorithms, a computationally efficient distributed rendezvous algorithm for a group of autonomous agents has been developed. The algorithm uses a rectilinear decision domain (RDD), as against the circular decision domain assumed in earlier work available in the literature. This helps in reducing its computational complexity considerably. An extensive mathematical analysis has been carried out to prove the convergence of the algorithm. The algorithm has also been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots.

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Books on the topic "Multi-agent paradigms"

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Roddy, Kimberly A. Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm. Monterey, Calif: Naval Postgraduate School, 2000.

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Perspective on Multi-Agent Systems: Paradigm, Formalism, Examples. Springer International Publishing AG, 2015.

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Modeling Tactical Level Combat Using a Multi-Agent System Design Paradigm (GI Agent). Storming Media, 2001.

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Modeling Bluetooth Radio Technology Simulation Using Multi-Agent Based System and Genetic Algorithm Design Paradigm. Storming Media, 2001.

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Book chapters on the topic "Multi-agent paradigms"

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Tuyls, K., and P. Stone. "Multiagent Learning Paradigms." In Multi-Agent Systems and Agreement Technologies, 3–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01713-2_1.

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Rybinski, Henryk, Dominik Ryżko, and Przemysław Więch. "Learning of Defaults by Agents in a Distributed Multi-Agent System Environment." In Emerging Paradigms in Machine Learning, 197–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28699-5_8.

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Poggi, Agostino, Giovanni Adorni, and Paola Turci. "Integrating Agent and Object-Oriented Programming Paradigms for Multi-Agent Systems Development." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 781. London: CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-139.

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Mušić, Denis. "Mechanism for Adaptation of Group Decision-making in Multi-agent E-Learning Environment." In E-Learning Paradigms and Applications, 251–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41965-2_9.

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Moise, Gabriela, Monica Vladoiu, and Zoran Constantinescu. "MASECO: A Multi-agent System for Evaluation and Classification of OERs and OCW Based on Quality Criteria." In E-Learning Paradigms and Applications, 185–227. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41965-2_7.

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Kachaoui, Jabrane, and Abdessamad Belangour. "Improving a New Data Lake Architecture Design Based on Data Ponds and Multi-Agent Paradigms." In Innovations in Smart Cities Applications Volume 5, 815–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94191-8_65.

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Amandi, Analía, Alejandro Zunino, and Ramiro Iturregui. "Multi-paradigm Languages Supporting Multi-agent Development." In Multi-Agent System Engineering, 128–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48437-x_11.

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Louvieris, Panos, Andreas Gregoriades, Natasha Mashanovich, Gareth White, Robert O’Keefe, Jerry Levine, and Stewart Henderson. "Agent-Based Parsimonious Decision Support Paradigm Employing Bayesian Belief Networks." In Defence Applications of Multi-Agent Systems, 24–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11683704_3.

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Chao, Sam, and Fai Wong. "A Multi-Agent Learning Paradigm for Medical Data Mining Diagnostic Workbench." In Data Mining and Multi-agent Integration, 177–86. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_12.

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Aguirre, Enrique, Arnulfo Alanis Garza, María del Rosario Baltazar, Lenin G. Lemus Zuñiga, Sergio Magdaleno Palencia, and Carlos Lino Ramírez. "Recognition of Primary Emotions Using the Paradigm of Intelligent Agents for the Recognition of Subtle Facial Expressions." In Agent and Multi-Agent Systems: Technologies and Applications, 345–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19728-9_29.

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Conference papers on the topic "Multi-agent paradigms"

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Garfield, Keith, Annie Wu, Mehmet Onal, Britt Crawford, Adam Campbell, and Randall Shumaker. "The Effectiveness of Transferring Multi-Agent Behaviors From a Learning Environment in the Presence of Synthetic Social Structures." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81890.

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The diverse behavior representation schemes and learning paradigms being investigated within the robotics community share the common feature that successful deployment of agents requires that behaviors developed in a learning environment are successfully applied to a range of unfamiliar and potentially more complex operational environments. The intent of our research is to develop insight into the factors facilitating successful transfer of behaviors to the operational environments. We present experimental results investigating the effects of several factors for a simulated swarm of autonomous vehicles. Our primary focus is on the impact of Synthetic Social Structures, which are guidelines directing the interactions between agents, much like social behaviors direct interactions between group members in the human and animal world. The social structure implemented is a dominance hierarchy, which has been shown previously to facilitate negotiation between agents. The goal of this investigation is to investigate mechanisms adding robustness to agent behavior.

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Picault, Sébastien, Yu-Lin Huang, Vianney Sicard, and Pauline Ezanno. "Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/53.

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Abstract:

The development of computational sciences has fostered major advances in life sciences, but also led to reproducibility and reliability issues, which become a crucial stake when simulations are aimed at assessing control measures, as in epidemiology. A broad use of software development methods is a useful remediation to reduce those problems, but preventive approaches, targeting not only implementation but also model design, are essential to sustainable enhancements. Among them, AI techniques, based on the separation between declarative and procedural concerns, and on knowledge engineering, offer promising solutions. Especially, multilevel multi-agent systems, deeply rooted in that culture, provide a generic way to integrate several epidemiological modeling paradigms within a homogeneous interface. We explain in this paper how this approach is used for building more generic, reliable and sustainable simulations, illustrated by real-case applications in cattle epidemiology.

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Sipele, Oscar, Victor Zamora, Agapito Ledezma, and Araceli Sanchis. "Advanced Driver's Alarms System through Multi-agent Paradigm." In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2018. http://dx.doi.org/10.1109/icite.2018.8492600.

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Fu, Haotian, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, and Changjie Fan. "Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces." 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.2019/323.

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Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (or parameterized) action spaces which is very common in practice. Our work fills this gap by proposing two novel algorithms: Deep Multi-Agent Parameterized Q-Networks (Deep MAPQN) and Deep Multi-Agent Hierarchical Hybrid Q-Networks (Deep MAHHQN). We follow the centralized training but decentralized execution paradigm: different levels of communication between different agents are used to facilitate the training process, while each agent executes its policy independently based on local observations during execution. Our empirical results on several challenging tasks (simulated RoboCup Soccer and game Ghost Story) show that both Deep MAPQN and Deep MAHHQN are effective and significantly outperform existing independent deep parameterized Q-learning method.

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Yuan, Lei, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. "Multi-Agent Concentrative Coordination with Decentralized Task Representation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/85.

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Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.

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Song, Sung Keun, SeungWok Han, and Hee Yong Youn. "A New Agent Platform Architecture Supporting the Agent Group Paradigm for Multi-Agent Systems." In 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07). IEEE, 2007. http://dx.doi.org/10.1109/iat.2007.33.

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Rachel, F. M., and P. S. Cugnasca. "The multi-agent programming paradigm use for railway applications." In COMPRAIL 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/cr080621.

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Lor, W. K. F., and K. Y. Szeto. "Switching dynamics of multi-agent systems: soap froths paradigm." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.859465.

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Li, En, Wei Lu, and Ying Zhou. "A Multi-agent Simulation Paradigm of Customer Retention Management." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.256.

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Zhang, Tianhao, Qiwei Ye, Jiang Bian, Guangming Xie, and Tie-Yan Liu. "MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/70.

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Abstract:

Value function decomposition (VFD) methods under the popular paradigm of centralized training and decentralized execution (CTDE) have promoted multi-agent reinforcement learning progress. However, existing VFD methods proceed from a group's value function decomposition to only solve cooperative tasks. With the individual value function decomposition, we propose MFVFD, a novel multi-agent Q-learning approach for solving cooperative and non-cooperative tasks based on mean-field theory. Our analysis on the Hawk-Dove and Nonmonotonic Cooperation matrix games evaluate MFVFD's convergent solution. Empirical studies on the challenging mixed cooperative-competitive tasks where hundreds of agents coexist demonstrate that MFVFD significantly outperforms existing baselines.

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