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Literatura académica sobre el tema "Multi Agent Systems"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Multi Agent Systems"

Gómez-Sanz, Jorge, and Juan Pavón. "Methodologies for Developing Multi-Agent Systems." JUCS - Journal of Universal Computer Science 10, no. (4) (April 28, 2004): 359–74. https://doi.org/10.3217/jucs-010-04-0359.

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As agent technology has matured with the deployment of a variety of applications, particularly in open and dynamic environments such as the web, several methodologies and tools have been proposed to support software engineers during the development process of such systems. This article takes an overall look at representative agent-oriented methodologies by considering how they support specific agent-related concepts. This serves to identify areas in which this technology has shown its potential to solve new problems, e.g., the ability to manage complexity with an organizational perspective, goal-driven modelling as a way to build robust behaviors for adaptive systems, or the definition of notation and mechanisms to implement high-level interactions and protocols between agents. In order to be fully applicable, the challenge today is the maturity of supporting tools, and new methods for validation and verification of multi-agent systems.

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Dastani, Mehdi. "Programming multi-agent systems." Knowledge Engineering Review 30, no. 4 (September 2015): 394–418. http://dx.doi.org/10.1017/s0269888915000077.

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AbstractWith the significant advances in the area of autonomous agents and multi-agent systems in the last decade, promising technologies for the development and engineering of multi-agent systems have emerged. The result is a variety of agent-oriented programming languages, development frameworks, execution platforms, and tools that facilitate building and engineering of multi-agent systems. This paper provides an overview of the multi-agent programming research field and explains the aim and characteristics of various multi-agent programming languages and development frameworks. This overview is complemented with a discussion on the current trends and challenges in this research community.

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SCHWEITZER, FRANK, and MATTHEW E. TAYLOR. "EDITORIAL: AGENTS AND MULTI-AGENT SYSTEMS." Advances in Complex Systems 14, no. 02 (April 2011): iii—iv. http://dx.doi.org/10.1142/s0219525911003025.

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Julian, Vicente, and Vicente Botti. "Multi-Agent Systems." Applied Sciences 9, no. 7 (April 3, 2019): 1402. http://dx.doi.org/10.3390/app9071402.

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With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.

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Brafman, R. I., and M. Tennenholtz. "On Partially Controlled Multi-Agent Systems." Journal of Artificial Intelligence Research 4 (June 1, 1996): 477–507. http://dx.doi.org/10.1613/jair.318.

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Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms, what methods can be applied to influence the uncontrollable agents, the effectiveness of such methods, and whether similar methods work across different domains. Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other they are reinforcement learners. We suggest different techniques for controlling agents' behavior in each domain, assess their success, and examine their relationship.

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BRAZIER, F. M. T., C. M. JONKER, J. TREUR, and N. J. E. WIJNGAARDS. "DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 11, no. 05 (October 2001): 559–81. http://dx.doi.org/10.1142/s0218194001000670.

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Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

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ALONSO, EDUARDO, MARK D'INVERNO, DANIEL KUDENKO, MICHAEL LUCK, and JASON NOBLE. "Learning in multi-agent systems." Knowledge Engineering Review 16, no. 3 (September 2001): 277–84. http://dx.doi.org/10.1017/s0269888901000170.

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In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

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JONG, STEVEN DE, KARL TUYLS, and KATJA VERBEECK. "Fairness in multi-agent systems." Knowledge Engineering Review 23, no. 2 (June 2008): 153–80. http://dx.doi.org/10.1017/s026988890800132x.

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AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

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Park, Sankyu, Key-Sun Choi, and K. H. (Kane) Kim. "A Framework for Multi-Agent Systems with Multi-Modal User Interfaces in Distributed Computing Environments." International Journal of Software Engineering and Knowledge Engineering 07, no. 03 (September 1997): 351–69. http://dx.doi.org/10.1142/s0218194097000217.

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In current multi-agent systems, the user is typically interacting with a single agent at a time through relatively inflexible and modestly intelligent interfaces. As a consequence, these systems force the users to submit simplistic requests only and suffer from problems such as the low-level nature of the system services offered to users, the weak reusability of agents, and the weak extensibility of the systems. In this paper, a framework for multi-agent systems called the open agent architecture (OAA) which reduces such problems, is discussed. The OAA is designed to handle complex requests that involve multiple agents. In some cases of complex requests from users, the components of the requests do not directly correspond to the capabilities of various application agents, and therefore, the system is required to translate the user's model of the task into the system's model before apportioning subtasks to the agents. To maximize users' efficiency in generating this type of complex requests, the OAA offers an intelligent multi-modal user interface agent which supports a natural language interface with a mix of spoken language, handwriting, and gesture. The effectiveness of the OAA environment including the intelligent distributed multi-modal interface has been observed in our development of several practical multi-agent systems.

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Yamada, Kazuaki, Kumiyo Nakakoji, and Kanji Ueda. "A Mutli-agent Systems Approach to Analyze Online Community Activities(Multi-agent and Learning,Session: TP2-A)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 51. http://dx.doi.org/10.1299/jsmeicam.2004.4.51_3.

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Tesis sobre el tema "Multi Agent Systems"

Lau, Pik Lik Billy. "Interdependence between agents in multi agent systems." Thesis, Curtin University, 2014. http://hdl.handle.net/20.500.11937/439.

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Interdependence relationships have defined the foundation of cooperation between agents but limited by existing protocols. As a consequence, the idle agents are not able to join and benefit through it. First, the inter-relationship has been studied and certified for the purpose of securing mutual gains. Next, the join coalition mechanism is proposed to help idle agents to join existing macroscopic and microscopic coalitions which are based on the goals compatibilities, budget and trust

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Dinu, Razvan. "Web Agents : towards online hybrid multi-agent systems." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20126/document.

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Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web

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Cardoso, Rafael Cau? "A decentralised online multi-agent planning framework for multi-agent systems." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8048.

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Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-05-08T18:37:11Z No. of bitstreams: 1 RAFAEL_CAU?_CARDOSO_TES.pdf: 14431785 bytes, checksum: 227194ed28a9e55c3ab1fbedebf06922 (MD5) Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2018-05-15T19:07:36Z (GMT) No. of bitstreams: 1 RAFAEL_CAU?_CARDOSO_TES.pdf: 14431785 bytes, checksum: 227194ed28a9e55c3ab1fbedebf06922 (MD5) Made available in DSpace on 2018-05-15T19:14:18Z (GMT). No. of bitstreams: 1 RAFAEL_CAU?_CARDOSO_TES.pdf: 14431785 bytes, checksum: 227194ed28a9e55c3ab1fbedebf06922 (MD5) Previous issue date: 2018-03-27 Sistemas multiagentes freq?entemente cont?m ambientes complexos e din?micos, nos quais os planos dos agentes podem falhar a qualquer momento durante a execu??o do sistema. Al?m disso, novos objetivos podem aparecer para os quais n?o existem nenhum plano dispon?vel. T?cnicas de planejamento s?o bem adequadas para lidar com esses problemas. H? uma quantidade extensa de pesquisa em planejamento centralizado para um ?nico agente, por?m, at? ent?o planejamento multiagente n?o foi completamente explorado na pr?tica. Plataformas multiagentes tipicamente proporcionam diversos mecanismos para coordena??o em tempo de execu??o, frequentemente necess?rios em planejamento online. Neste contexto, planejamento multiagente descentralizado pode ser eficiente e eficaz, especialmente em dom?nios fracamente acoplados, al?m de garantir algumas propriedades importantes em sistemas de agentes como privacidade e autonomia. N?s abordamos esse problema ao apresentar uma t?cnica para planejamento multiagente online que combina aloca??o de objetivos, planejamento individual utilizando rede de tarefas hier?rquicas (HTN), e coordena??o em tempo de execu??o para apoiar a realiza??o de objetivos sociais em sistemas multiagentes. Especificamente, n?s apresentamos um framework chamado Decentralised Online Multi-Agent Planning (DOMAP). Experimentos com tr?s dom?nios fracamente acoplados demonstram que DOMAP supera quatro planejadores multiagente do estado da arte com respeito a tempo de planejamento e tempo de execu??o, particularmente nos problemas mais dif?ceis. Multi-agent systems often contain dynamic and complex environments where agents? course of action (plans) can fail at any moment during execution of the system. Furthermore, new goals can emerge for which there are no known plan available in any of the agents? plan library. Automated planning techniques are well suited to tackle both of these issues. Extensive research has been done in centralised planning for singleagents, however, so far multi-agent planning has not been fully explored in practice. Multi-agent platforms typically provide various mechanisms for runtime coordination, which are often required in online planning (i.e., planning during runtime). In this context, decentralised multi-agent planning can be efficient as well as effective, especially in loosely-coupled domains, besides also ensuring important properties in agent systems such as privacy and autonomy. We address this issue by putting forward an approach to online multi-agent planning that combines goal allocation, individual Hierarchical Task Network (HTN) planning, and coordination during runtime in order to support the achievement of social goals in multi-agent systems. In particular, we present a planning and execution framework called Decentralised Online Multi-Agent Planning (DOMAP). Experiments with three loosely-coupled planning domains show that DOMAP outperforms four other state-of-the-art multi agent planners with regards to both planning and execution time, particularly in the most difficult problems.

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Easwaran, Aneurin Maatchiman. "Federation in multi-agent systems." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430853.

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Raimondi, Franco. "Model checking multi-agent systems." Thesis, University College London (University of London), 2006. http://discovery.ucl.ac.uk/5627/.

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A multi-agent system (MAS) is usually understood as a system composed of interacting autonomous agents. In this sense, MAS have been employed successfully as a modelling paradigm in a number of scenarios, especially in Computer Science. However, the process of modelling complex and heterogeneous systems is intrinsically prone to errors: for this reason, computer scientists are typically concerned with the issue of verifying that a system actually behaves as it is supposed to, especially when a system is complex. Techniques have been developed to perform this task: testing is the most common technique, but in many circumstances a formal proof of correctness is needed. Techniques for formal verification include theorem proving and model checking. Model checking techniques, in particular, have been successfully employed in the formal verification of distributed systems, including hardware components, communication protocols, security protocols. In contrast to traditional distributed systems, formal verification techniques for MAS are still in their infancy, due to the more complex nature of agents, their autonomy, and the richer language used in the specification of properties. This thesis aims at making a contribution in the formal verification of properties of MAS via model checking. In particular, the following points are addressed: • Theoretical results about model checking methodologies for MAS, obtained by extending traditional methodologies based on Ordered Binary Decision Diagrams (OBDDS) for temporal logics to multi-modal logics for time, knowledge, correct behaviour, and strategies of agents. Complexity results for model checking these logics (and their symbolic representations). • Development of a software tool (MCMAS) that permits the specification and verification of MAS described in the formalism of interpreted systems. • Examples of application of MCMAS to various MAS scenarios (communication, anonymity, games, hardware diagnosability), including experimental results, and comparison with other tools available.

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Huang, Zhan. "Mutation for multi-agent systems." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/19270/.

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Although much progress has been made in engineering multi-agent systems (MAS), many issues remain to be resolved. One issue is that there is a lack of techniques that can adequately evaluate the effectiveness (fault detection ability) of tests or testing techniques for MAS. Another is that there are no systematic approaches to evaluating the impact of possible semantic changes (changes in the interpretation of agent programs) on agents' behaviour and performance. This thesis introduces syntactic and semantic mutation to address these two issues. Syntactic mutation is a technique that systematically generates variants ("syntactic mutants") of a description (usually a program) following a set of rules ("syntactic mutation operators"). Each mutant is expected to simulate a real description fault, therefore, the effectiveness of a test set can be evaluated by checking whether it can detect each simulated fault, in other words, distinguish the original description from each mutant. Although syntactic mutation is widely considered very effective, only limited work has been done to introduce it into MAS. This thesis extends syntactic mutation for MAS by proposing a set of syntactic mutation operators for the Jason agent language and showing that they can be used to generate real faults in Jason agent programs. By contrast, semantic mutation systematically generates variant interpretations ("semantic mutants") of a description following a set of rules ("semantic mutation operators"). Semantic mutation has two uses: to evaluate the effectiveness of a test set by simulating faults caused by misunderstandings of how the description is interpreted, and to evaluate the impact of possible semantic changes on agents' behaviour and performance. This thesis, for the first time, proposes semantic mutation for MAS, more specifically, for three logic based agent languages, namely Jason, GOAL and 2APL. It proposes semantic mutation operators for these languages, shows that the operators for Jason can represent real misunderstandings and are practically useful.

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Haque, Musad Al. "Biologically inspired heterogeneous multi-agent systems." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37138.

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Many biological systems are known to accomplish complex tasks in a decentralized, robust, and scalable manner - characteristics that are desirable to the coordination of engineered systems as well. Inspired by nature, we produce coordination strategies for a network of heterogenous agents and in particular, we focus on intelligent collective systems. Bottlenose dolphins and African lions are examples of intelligent collective systems since they exhibit sophisticated social behaviors and effortlessly transition between functionalities. Through preferred associations, specialized roles, and self-organization, these systems forage prey, form alliances, and maintain sustainable group sizes. In this thesis, we take a three-phased approach to bioinspiration: in the first phase, we produce agent-based models of specific social behaviors observed in nature. The goal of these models is to capture the underlying biological phenomenon, yet remain simple so that the models are amenable to analysis. In the second phase, we produce bio-inspired algorithms that are based on the simple biological models produced in the first phase. Moreover, these algorithms are developed in the context of specific coordination tasks, e.g., the multi-agent foraging task. In the final phase of this work, we tailor these algorithms to produce coordination strategies that are ready to be deployed in target applications.

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Gill, Martin L. "Combining MAS and P2P systems : the Agent Trees Multi-Agent System (ATMAS)." Thesis, University of Stirling, 2005. http://hdl.handle.net/1893/108.

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The seamless retrieval of information distributed across networks has been one of the key goals of many systems. Early solutions involved the use of single static agents which would retrieve the unfiltered data and then process it. However, this was deemed costly and inefficient in terms of the bandwidth since complete files need to be downloaded when only a single value is often all that is required. As a result, mobile agents were developed to filter the data in situ before returning it to the user. However, mobile agents have their own associated problems, namely security and control. The Agent Trees Multi-Agent System (AT-MAS) has been developed to provide the remote processing and filtering capabilities but without the need for mobile code. It is implemented as a Peer to Peer (P2P) network of static intelligent cooperating agents, each of which control one or more data sources. This dissertation describes the two key technologies have directly influenced the design of ATMAS, Peer-to-Peer (P2P) systems and Multi-Agent Systems (MAS). P2P systems are conceptually simple, but limited in power, whereas MAS are significantly more complex but correspondingly more powerful. The resulting system exhibits the power of traditional MAS systems while retaining the simplicity of P2P systems. The dissertation describes the system in detail and analyses its performance.

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Rahwan, Iyad. "Interest-based negotiation in multi-agent systems." Connect to thesis, 2004. http://repository.unimelb.edu.au/10187/2885.

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Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)

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Seyboth, Georg Sebastian. "Event-based Controlfor Multi-Agent Systems." Thesis, KTH, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105117.

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In this thesis, a novel approach to the average consensus problem for multi-agent systems is followed. A new event-based control strategy is proposed, which incorporates event-based scheduling of state measurement broadcasts over the network. The control-laws are based on the resulting piecewise constant functions of these measurement values. This facilitates implementation on digital platforms such as microprocessors and reduces the number of interagent communications over the network. Starting from a basic problem setup with singleintegrator agents, fixed undirected connected communication topologies, and no time-delays, the novel strategy is developed. Different triggering conditions guaranteeing convergence to an adjustable region around the average consensus point or asymptotic convergence to this point, respectively, are discussed. Numerical simulations show the effectiveness of this approach, outperforming classical time-scheduled implementations of the consensus protocol in terms of load on the communication medium. Furthermore the problem class is extended to networks with directed communication links, switching topologies, and time-delays in the communication as well as to agents with double-integrator dynamics. As an illustrative example, the novel strategy is applied to a formation control problem of non-holonomic mobile robots in the plane.

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Libros sobre el tema "Multi Agent Systems"

Wagner, Tom, and Omer F. Rana, eds. Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1.

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Rosenfeld, Ariel, and Nimrod Talmon, eds. Multi-Agent Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82254-5.

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Bulling, Nils, ed. Multi-Agent Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17130-2.

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Slavkovik, Marija, ed. Multi-Agent Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14174-5.

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Cossentino, Massimo, Michael Kaisers, Karl Tuyls, and Gerhard Weiss, eds. Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34799-3.

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Baumeister, Dorothea, and Jörg Rothe, eds. Multi-Agent Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20614-6.

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Malvone, Vadim, and Aniello Murano, eds. Multi-Agent Systems. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43264-4.

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1967-, Alonso Eduardo, Kudenko Daniel 1968-, and Kazakov Dimitar 1967-, eds. Adaptive agents and multi-agent systems, adaptation and multi-agent learning. Berlin: Springer, 2003.

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Alonso, Eduardo, Daniel Kudenko, and Dimitar Kazakov, eds. Adaptive Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44826-8.

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Bui, The Duy, Tuong Vinh Ho, and Quang Thuy Ha, eds. Intelligent Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6.

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Capítulos de libros sobre el tema "Multi Agent Systems"

Pernpeintner, Michael, Christian Bartelt, and Heiner Stuckenschmidt. "Governing Black-Box Agents in Competitive Multi-Agent Systems." In Multi-Agent Systems, 19–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82254-5_2.

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Gleizes, Marie-Pierre. "Self-adaptive Complex Systems." In Multi-Agent Systems, 114–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34799-3_8.

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Jarvis, Dennis, Jacqueline Jarvis, Ralph Rönnquist, and Lakhmi C. Jain. "Multi-Agent Systems." In Multiagent Systems and Applications, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33320-0_1.

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Shekhar, Shashi, and Hui Xiong. "Multi Agent Systems." In Encyclopedia of GIS, 747. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_838.

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Benmammar, Badr, and Asma Amraoui. "Multi-Agent Systems." In Radio Resource Allocation and Dynamic Spectrum Access, 39–52. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.ch3.

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Pérez Castaño, Arnaldo. "Multi-Agent Systems." In Practical Artificial Intelligence, 193–220. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_5.

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Burguillo, Juan C. "Multi-agent Systems." In Self-organizing Coalitions for Managing Complexity, 69–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69898-4_5.

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Mes, Martijn, and Berry Gerrits. "Multi-agent Systems." In Operations, Logistics and Supply Chain Management, 611–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92447-2_27.

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Lunze, J., F. Allgöwer, M. Bürger, O. Demir, U. Helmke, A. von Heusinger, and R. Schuh. "Multi-agent Systems." In Control Theory of Digitally Networked Dynamic Systems, 263–324. Heidelberg: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01131-8_6.

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Gleizes, Marie-Pierre, Valérie Camps, Anthony Karageorgos, and Giovanna Di Marzo Serugendo. "Agents and Multi-Agent Systems." In Natural Computing Series, 105–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17348-6_5.

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Actas de conferencias sobre el tema "Multi Agent Systems"

"Table of contents." In Multi-Agent Systems. IEEE, 2011. http://dx.doi.org/10.1109/iama.2011.6048987.

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"Agents and multi-agent systems." In 8th International Conference on Computer Supported Cooperative Work in Design. IEEE, 2004. http://dx.doi.org/10.1109/cacwd.2004.1349032.

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"USING ROBOTIC SYSTEMS IN A SMART HOUSE FOR PEOPLE WITH DISABILITIES." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001502902810286.

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"ROBOTIC SOCCER: THE GATEWAY FOR POWERFUL ROBOTIC APPLICATIONS." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001503202870293.

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"CONCEPTS FOR AUTONOMOUS COMMAND AND CONTROL." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001507403220329.

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"COLLABORATION VS. OBSERVATION: AN EMPIRICAL STUDY IN MULTIAGENT PLANNING UNDER RESOURCE CONSTRAINT." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001508702940300.

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"TRILATERATION LOCALIZATION FOR MULTI-ROBOT TEAMS." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001508803010307.

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"POTENTIAL FIELD BASED INTEGRATED EXPLORATION FOR MULTI-ROBOT TEAMS." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001508903080314.

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"A GENERIC ARCHITECTURE FOR A COMPANION ROBOT." In Multi-Agent Robotic Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001509103150321.

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Gawali, R. D., and B. B. Meshram. "Agent-based autonomous Examination Systems." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228095.

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Informes sobre el tema "Multi Agent Systems"

NAVAL RESEARCH LAB WASHINGTON DC. Evolvability of Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444826.

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Marik, Vladimir, Michal Pechoucek, David Sislak, Martin Rehak, Jiri Lazansky, and Jan Tozicka. Inaccessibility in Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada425140.

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Luke, Sean, and Jeffrey K. Bassett. Adaptive and Robust Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada483725.

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Lerman, Kristina, Maja Mataric, and Aram Galstyan. Mathematical Modeling of Large Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada439172.

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Zilberstein, Shlomo. Decision-Theoretic Foundations for Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada567155.

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Fang, Frank, Elaine Reed, David K. Dickason, H. J. Simien, and Michelle L. Wulff. Technology Review of Multi-Agent Systems and Tools. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada434259.

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Hovakimyan, Naira. Robust Architectures for Complex Multi-Agent Heterogeneous Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada610191.

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Nissen, Mark E. Virtual Supply Chain Re-Intermediation Through Multi-Agent Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada408668.

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Ghose, Debasish. Broadcast Based Control of Multi-Agent Systems for Consensus. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada533400.

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Shoham, Yoav. Communication and Coordination in Multi-Agent Systems: Agent-Oriented Programming and Computational Social Laws. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada329826.

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