Relevant bibliographies by topics / Autonomous agents and multiagent systems

Academic literature on the topic 'Autonomous agents and multiagent systems'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Autonomous agents and multiagent systems"

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Ur Rehman, Shafiq, and Aamer Nadeem. "An Approach to Model Based Testing of Multiagent Systems." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925206.

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Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.
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Endriss, Ulle, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (September 2021): 29–37. http://dx.doi.org/10.1145/3511322.3511329.

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The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.
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Dahlstedt, Palle, and Peter McBurney. "Musical Agents: Toward Computer-Aided Music Composition Using Autonomous Software Agents." Leonardo 39, no. 5 (October 2006): 469–70. http://dx.doi.org/10.1162/leon.2006.39.5.469.

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The authors, a composer and a computer scientist, discuss their collaborative research on the use of multiagent systems and their applicability to music and musical composition. They describe the development of software and techniques for the composition of generative music.
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Muñoz, Antonio, Pablo Anton, and Antonio Maña. "Multiagent Systems Protection." Advances in Software Engineering 2011 (August 15, 2011): 1–9. http://dx.doi.org/10.1155/2011/281517.

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Agent-systems can bring important benefits especially in applications scenarios where highly distributed, autonomous, intelligence, self-organizing, and robust systems are required. Furthermore, the high levels of autonomy and self-organizations of agent systems provide excellent support for developments of systems in which dependability is essential. Both Ubiquitous Computing and Ambient Intelligence scenarios belong in this category. Unfortunately, the lack of appropriate security mechanisms, both their enforcement and usability, is hindering the application of this paradigm in real-world applications. Security issues play an important role in the development of multiagent systems and are considered to be one of the main issues to solve before agent technology is ready to be widely used outside the research community. In this paper, we present a software based solution for the protection of multiagent systems concentrating on the cooperative agents model and the protected computing approach.
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Hyso, Alketa, and Eva Cipi. "Autonomous Agents as Tools for Modeling and Building Complex Control Systems that Operate in Dynamic and Unpredictable Environment." International Journal of Business & Technology 1, no. 2 (May 2013): 47–53. http://dx.doi.org/10.33107/ijbte.2013.1.2.05.

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Complex control systems that operate in not entirely predictable environment have to deal with this environment in an autonomous manner using adaptability, the ability to predict environmental changes, and to maintain their integrity. Elements of the system must be able to find a new solution in a dynamic way. In this paper, we present the modeling of a traffic lights’ control system using a multivalent system. This is a large-scale distributed system, consisting of autonomous and rational traffic light agents, in which there is no centre imposing an outcome. Multiagent system brings another kind of organization of the distributed control. The information is distributed over the agents. The behavior of the other agents is incorporated into the making decision process of the agent. We apply different control algorithms in our multiagent simulation environment and show that using multiagent systems in dynamic and unpredictable environment the control will be adoptable.
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Mackin, Kenneth James. "Autonomous Learning of Agent Communication and Group Behavior in Intelligent Multiagent Systems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 2 (2003): 187. http://dx.doi.org/10.3156/jsoft.15.187_2.

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Silva, Felipe Leno Da, and Anna Helena Reali Costa. "A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems." Journal of Artificial Intelligence Research 64 (March 11, 2019): 645–703. http://dx.doi.org/10.1613/jair.1.11396.

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Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.
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Sonenberg, Liz, Peter Stone, Kagan Tumer, and Pinar Yolum. "Ten Years of AAMAS: Introduction to the Special Issue." AI Magazine 33, no. 3 (September 20, 2012): 11. http://dx.doi.org/10.1609/aimag.v33i3.2423.

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Panella, Alessandro. "Multiagent Stochastic Planning With Bayesian Policy Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1672–73. http://dx.doi.org/10.1609/aaai.v27i1.8506.

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When operating in stochastic, partially observable, multiagent settings, it is crucial to accurately predict the actions of other agents. In my thesis work, I propose methodologies for learning the policy of external agents from their observed behavior, in the form of finite state controllers. To perform this task, I adopt Bayesian learning algorithms based on nonparametric prior distributions, that provide the flexibility required to infer models of unknown complexity. These methods are to be embedded in decision making frameworks for autonomous planning in partially observable multiagent systems.
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Satybaldiyeva, A., A. Ismailova, R. Moldasheva, A. Mukhanova, and K. Kadirkulov. "ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS." PHYSICO-MATHEMATICAL SERIES 2, no. 336 (April 15, 2021): 48–55. http://dx.doi.org/10.32014/2021.2518-1726.20.

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Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.
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Dissertations / Theses on the topic "Autonomous agents and multiagent systems"

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Hurt, David. "An Empirical Evaluation of Communication and Coordination Effectiveness in Autonomous Reactive Multiagent Systems." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4770/.

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This thesis describes experiments designed to measure the effect of collaborative communication on task performance of a multiagent system. A discrete event simulation was developed to model a multi-agent system completing a task to find and collect food resources, with the ability to substitute various communication and coordination methods. Experiments were conducted to find the effects of the various communication methods on completion of the task to find and harvest the food resources. Results show that communication decreases the time required to complete the task. However, all communication methods do not fare equally well. In particular, results indicate that the communication model of the bee is a particularly effective method of agent communication and collaboration. Furthermore, results indicate that direct communication with additional information content provides better completion results. Cost-benefit models show some conflicting information, indicating that the increased performance may not offset the additional cost of achieving that performance.
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Oyenan, Walamitien Hervé. "An algebraic framework for compositional design of autonomous and adaptive multiagent systems." Diss., Kansas State University, 2010. http://hdl.handle.net/2097/3869.

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Doctor of Philosophy
Department of Computing and Information Sciences
Scott A. DeLoach
Organization-based Multiagent Systems (OMAS) have been viewed as an effective paradigm for addressing the design challenges posed by today’s complex systems. In those systems, the organizational perspective is the main abstraction, which provides a clear separation between agents and systems, allowing a reduction in the complexity of the overall system. To ease the development of OMAS, several methodologies have been proposed. Unfortunately, those methodologies typically require the designer to handle system complexity alone, which tends to lead to ad-hoc designs that are not scalable and are difficult to maintain. Moreover, designing organizations for large multiagent systems is a complex and time-consuming task; design models quickly become unwieldy and thus hard to develop. To cope with theses issues, a framework for organization-based multiagent system designs based on separation of concerns and composition principles is proposed. The framework uses category theory tools to construct a formal composition framework using core models from the Organization-based Multiagent Software Engineering (O-MASE) framework. I propose a formalization of these models that are then used to establish a reusable design approach for OMAS. This approach allows designers to design large multiagent organizations by reusing smaller composable organizations that are developed separately, thus providing them with a scalable approach for designing large and complex OMAS. In this dissertation, the process of formalizing and composing multiagent organizations is discussed. In addition, I propose a service-oriented approach for building autonomous, adaptive multiagent systems. Finally, as a proof of concept, I develop two real world examples from the domain of cooperative robotics and wireless sensor networks.
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Davis, Cledo L. "The systems integration of autonomous behavior analysis to create a "Maritime Smart Environment" for the enhancement of maritime domain awareness." Thesis, Monterey, California : Naval Postgraduate School, 2010. http://edocs.nps.edu/npspubs/scholarly/theses/2010/Jun/10Jun%5FDavis.pdf.

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Thesis (M.S. in Systems Engineering)--Naval Postgraduate School, June 2010.
Thesis Advisor(s): Goshorn, Rachel ; Goshorn, Deborah. "June 2010." Description based on title screen as viewed on June 24, 2010. Author(s) subject terms: Anomaly Detection, Artificial Intelligence, Automation, Behavior Analysis, Distributed Artificial Intelligence, Intelligence-Surveillance-Reconnaissance, Maritime Domain Awareness, Maritime Force Protection, Multi-agent Systems, Network-centric Operations, Network-centric Systems Engineering, Network-centric Warfare, Smart Sensor Networks, Systems Engineering, Systems Integration, System of Systems. Includes bibliographical references (p. 209-212). Also available in print.
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Seppecher, Pascal. "Modélisation multi-agents d'une économie monétaire de production : un système dynamique et complexe d'interactions réelles et monétaires entre des agents multiples, hétérogènes, autonomes et concurrents." Phd thesis, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00693151.

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Nous présentons un modèle numérique d'économie de marché décentralisée, fonctionnant hors de l'équilibre, composée de deux grands groupes d'agents (entreprises et ménages) auxquels sont respectivement associées deux fonctions économiques principales (production et consommation). Ces fonctions s'exercent dans le respect des règles des économies capitalistes (propriété privée des moyens de production, échanges monétaires, salariat). Les agents sont des individus en interaction directe et indirecte (et non des agents représentatifs ou des agrégats), chacun poursuivant son propre but, agissant en fonction de son état individuel et de l'environnement proche, sans se préoccuper du tout de l'équilibre général du système et sans contrôle supérieur (ni de la part d'un planificateur, ni d'un commissaire-priseur). Le modèle respecte les trois principes essentiels des économies monétaires: - le processus de production prend du temps et les entreprises ont besoin de crédit pour lancer ce processus; - la monnaie est endogène, elle est créée par le crédit bancaire à la production; - les entreprises sont guidées par le motif de profit et ce profit est monétaire. Implémenté en Java, le modèle se présente comme un laboratoire virtuel permettant de conduire de véritables expériences "in silico". Les interactions réelles et monétaires entre les agents (multiples, hétérogènes, autonomes et concurrents) forment un système dynamique et complexe dont les propriétés macroéconomiques ne sont pas postulées. Nous reconstruisons ainsi les conditions de l'émergence de comportements macroéconomiques inobservables dans les modèles basés sur les notions d'agent représentatif et d'équilibre général.
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Seelam, Aruntej. "REORGANIZATION OF MASSIVE MULTIAGENT SYSTEMS: MOTL/O." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1966550041&sid=3&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Oyenan, Walamitien Hervé. "An algebraic framework for compositional design of autonomous and adaptive multiagent systems." Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3869.

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Jarne, Ornia Daniel. "Ant Colony Algorithms andits applications to Autonomous Agents Systems." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217391.

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With the latest advancements in autonomous agents systems and technology, there is a growing interest in developing control algorithms and methods to coordinate large numbers of robotic entities. Following this line of work, the use of biologically inspired algorithms based on swarm emerging behaviour presents some really interesting properties for controlling multiple agents. They rely on very simple instructions and communications to develop a coordinated structure in the system. Particularly, this master thesis focuses on the study of Ant Colony algorithms based on stigmergy interaction to coordinate agents and perform a certain task. The first part focuses on the theoretical background and algorithm convergence proof, while the second part consists of experimental simulations and results. For this, some metric parameters have been developed and found to be especially useful in the study of a simple path planning test case. The main concept developed in this work is an adaptation of Shannon Entropy that measures uniformity and order in the system and the weighted graph. This parameter has been used to study the performance and results of an autonomous agent system based on Ant Colony algorithms. Finally, this control algorithm has been modified to develop an event-triggered control scheme. Using the properties of the weighted graph (Entropy) and the sensing of the agents, a decentralized event-triggered method has been implemented and tested, and has been found to increase efficiency in the usage of system resources.
Med den senaste tidens utveckling inom autonoma agentsystem och teknologier, finns ett ökat intresse för utveckling av styralgoritmer och metoder för att koordinera stora mängder robotenheter. Inom detta område visar användandet av biologiskt inspirerade algoritmer, baserade på naturliga svärmbeteenden, intressanta egenskaper som kan utnyttjas i styrandet av system som innefattar ett flertal agenter. Dessa är uppbyggda av simpla instruktioner och kommunikationsmedel för att tillgodose struktur i systemet. I synnerhet fokuserar detta masterexamensarbete på studier av Ant Colony-algoritmer, baserade på stigmergy-interaktion för att koordinera enheter och få dem att utföra specifika uppgifter. Den första delen behandlar den teoretiska bakgrunden och konvergensbevis medan den andra delen i huvudsak består av experimentella simuleringar samt resultat. Till detta ändamål har metriska parametrar utvecklats, vilka ansågs särskilt användbara när planeringen av en enkel bana studerades. Huvudkonceptet som utvecklats i detta arbete är en tillämpning av Shannon- Entropi, vilket mäter enhetlighet och ordning i ett system samt den viktade grafen. Denna parameter har använts för att studera prestandan och resultaten hos ett autonomt agentsystem baserat på Ant Colony-algoritmer. Slutligen har denna styralgoritm modifierats för att utveckla ett händelsestyrt styrschema. Genom att använda egenskaperna hos den viktade grafen (entropi) tillsammans med sensorsystemet hos agentenheterna, så har en decentraliserad händelsestyrd metod implementerats, testats och visat sig ge ökad effektivitet gällande utnyttjandet av systemresurser.
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AL-Buraiki, Omar S. M. "Specialized Agents Task Allocation in Autonomous Multi-Robot Systems." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41504.

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With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks. This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics. The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes. Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.
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Hess, Traci J. "A Study of Autonomous Agents in Decision Support Systems." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/11277.

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Software agents have been heralded as the most important emerging technology of the decade. As software development firms eagerly attempt to integrate these autonomous programs into their products, researchers attempt to define the concept of agency and to develop architectures that will improve agent capabilities. Decision Support System (DSS) researchers have been eager to integrate agents into their applications, and exploratory works in which agents have been used within a DSS have been documented. This dissertation attempts to further this exploration by studying the agent features and underlying architectures that can lead to the successful integration of agents in DSS. This exploration is carried out in three parts. In the first part, a review of the relevant research streams is provided. The history and current status of software agents is first discussed. Similarly, a historical and current view of DSS research is provided. Lastly, a historical and tutorial-type of discussion is provided on the topic of Artificial Intelligence (AI) planning. This review of the relevant literature provides a general background for the conceptual analyses and implementations that are carried out in the next two sections. In the second part, the literature on software agents is synthesized to develop a definition of agency applicable to DSS. Using this definition, an agent-integrated DSS that supports variance-analysis is designed and developed. Following this implementation, a general framework for agent-enabling DSS is suggested. The use of this framework promises to raise some DSS to a new level of capability whereby "what-if" systems are transformed into real-time, proactive systems. The third part utilizes this general framework to agent-enable a corporate-planning system DSS and extends the framework in the second section through the introduction of an automated-planning agent. The agent uses AI planning to generate decision-making alternatives, providing a means to integrate and sequence the models in the DSS. The architecture used to support this planning agent is described. This new kind of DSS enables not only the monitoring of goals, but also the maintenance of these goals through agent-generated plans. The conclusion summarizes the contributions of this work and outlines in considerable detail potential research opportunities in the realm of software agents, DSS, and planning.
Ph. D.
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Khan, Majid Ali. "Coalition formation and teamwork in embodied agents." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2711.

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Embodied agents are agents acting in the physical world, such as persons, robots, unmanned air or ground vehicles and so on. These types of agents are subject to spatio-temporal constraints, which do not exist for agents acting in a virtual environment. The movement of embodied agents is limited by obstacles and maximum velocity, while their communication is limited by the transmission range of their wireless devices. This dissertation presents contributions to the techniques of coalition formation and teamwork coordination for embodied agents. We considered embodied agents in three different settings, each of them representative of a class of practical applications. First, we study coalition formation in the one dimensional world of vehicles driving on a highway. We assume that vehicles can communicate over short distances and carry agents which can advise the driver on convoy formation decisions. We introduce techniques which allow vehicles to influence the speed of the convoys, and show that this yields convoys which have a higher utility for the participating vehicles. Second, we address the problem of coalition formation in the two dimensional world. The application we consider is a disaster response scenario. The agents are forming coalitions through a multi-issue negotiation with spatio-temporal components where the coalitions maintain a set of commitments towards participating agents. Finally, we discuss a scenario where embodied agents form coalitions to optimally address dynamic, non-deterministic, spatio-temporal tasks. The application we consider is firefighters acting in a disaster struck city.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
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Books on the topic "Autonomous agents and multiagent systems"

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Osman, Nardine, and Carles Sierra, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46840-2.

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Sukthankar, Gita, and Juan A. Rodriguez-Aguilar, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71679-4.

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Sukthankar, Gita, and Juan A. Rodriguez-Aguilar, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71682-4.

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Osman, Nardine, and Carles Sierra, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2.

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Schuldt, Arne. Multiagent Coordination Enabling Autonomous Logistics. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Melo, Francisco S., and Fei Fang, eds. Autonomous Agents and Multiagent Systems. Best and Visionary Papers. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20179-0.

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(Italy), International Joint Conference on Autonomous Agents and Multiagent Systems (1st 2002 Bologna. Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: July 15-19, 2002, Plazzo re Enzo, Bologna, Italy. New York: ACM Press, 2002.

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International Joint Conference on Autonomous Agents and Multiagent Systems (2nd 2003 Melbourne, Australia). AAMAS03: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent System : July 14-18, 2003, Melbourne, Australia. Edited by Rosenschein Jeffrey S and SIGART. [New York]: ACM Press, 2003.

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1952-, Conte Rosaria, Dellarocas Chrysanthos 1967-, and Workshop on Norms and Institutions in Multi-Agent Systems (1st : 2000 : Barcelona, Spain), eds. Social order in multiagent systems. Boston: Kluwer Academic Publishers, 2001.

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An introduction to multiagent systems. Chichester, England: J. Wiley, 2002.

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Book chapters on the topic "Autonomous agents and multiagent systems"

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Rafique, Umair, and Shell Ying Huang. "Preference Generation for Autonomous Agents." In Multiagent System Technologies, 173–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16178-0_17.

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Baldeón, J., M. Lopez-Sanchez, I. Rodríguez, and A. Puig. "Gamification of Multi-agent Systems Theory Classes." In Autonomous Agents and Multiagent Systems, 172–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46840-2_11.

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Luo, Jieting, John-Jules Meyer, and Max Knobbout. "Reasoning About Opportunistic Propensity in Multi-agent Systems." In Autonomous Agents and Multiagent Systems, 203–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71682-4_13.

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Durfee, Edmund H., and Satinder Singh. "On the Trustworthy Fulfillment of Commitments." In Autonomous Agents and Multiagent Systems, 1–13. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_1.

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Ikeda, Keisuke, Takeshi Sakaki, Fujio Toriumi, and Satoshi Kurihara. "An Examination of a Novel Information Diffusion Model: Considering of Twitter User and Twitter System Features." In Autonomous Agents and Multiagent Systems, 180–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_11.

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Crawford, Chad, Zenefa Rahaman, and Sandip Sen. "Evaluating the Efficiency of Robust Team Formation Algorithms." In Autonomous Agents and Multiagent Systems, 14–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_2.

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Christodoulou, George, Aris Filos-Ratsikas, Søren Kristoffer Stiil Frederiksen, Paul W. Goldberg, Jie Zhang, and Jinshan Zhang. "Social Welfare in One-Sided Matching Mechanisms." In Autonomous Agents and Multiagent Systems, 30–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_3.

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Amigoni, Francesco, Andrea Castelletti, Paolo Gazzotti, Matteo Giuliani, and Emanuele Mason. "Using Multiagent Negotiation to Model Water Resources Systems Operations." In Autonomous Agents and Multiagent Systems, 51–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_4.

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Oldham, Matthew. "To Big Wing, or Not to Big Wing, Now an Answer." In Autonomous Agents and Multiagent Systems, 73–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_5.

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Winikoff, Michael. "How Testable Are BDI Agents? An Analysis of Branch Coverage." In Autonomous Agents and Multiagent Systems, 90–106. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2_6.

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Conference papers on the topic "Autonomous agents and multiagent systems"

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Montes, Nieves. "Engineering Socially-Oriented Autonomous Agents and Multiagent Systems." 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.2022/833.

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The emergent field of social AI is concerned with the development of autonomous agents that are able to act as part of larger community. Within this context, my research seeks to engineer meaningful social interactions among a group of agents from two different approaches. First, the societal level leverages constructs that apply to a society as a whole, like norms and social values. Second, the individual level endows agents with the ability to reason about others, by making use of Theory of Mind capabilities.
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"Session details: Posters." In AAMAS03: Second International Conference on Autonomous Agents and Multiagent Systems. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3249227.

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Rana, Omer, and Thomas Rist. "Session details: Demos." In AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3246194.

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Pechoucek, Michal, Donald Steiner, and Simon Thompson. "Session details: Industrial track: logistics & transport." In AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3246192.

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Pechoucek, Michal, Donald Steiner, and Simon Thompson. "Session details: Industrial track: aerospace applications." In AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3246191.

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Dix, Jurgen. "Session details: Doctoral mentoring students." In AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3246190.

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Pechoucek, Michal, Donald Steiner, and Simon Thompson. "Session details: Industrial track: manufacturing." In AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3246193.

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Takano, R., D. Yamazaki, Y. Ichikawa, K. Hattori, and K. Takadama. "Multiagent-based ABC algorithm for Autonomous Rescue Agent Cooperation." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6973971.

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Soria, Nicolás F., Mitchell K. Colby, Irem Y. Tumer, Christopher Hoyle, and Kagan Tumer. "Design of Complex Engineering Systems Using Multiagent Coordination." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59570.

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In complex engineering systems, complexity may arise by design, or as a by-product of the system’s operation. In either case, the root cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled; and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multiagent coordination problem, where component decisions and their interactions lead to global behavior. The design of a race car is used as the case study. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a Formula racing vehicle.
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Premm, Marc, and Stefan Kirn. "Autonomous Agents in Multiagent Organizations." In 9th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006094901210128.

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Reports on the topic "Autonomous agents and multiagent systems"

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Shen, Wei-Min. Self-Organizing and Autonomous Learning Agents and Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada430491.

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