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Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.


Papers
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Book
30 Sep 1997
TL;DR: The IMAHDA Revisited, a meta-modelling framework for knowledge discovery and data mining in the Animal Domain, and the RTAPS Implementation, a model for the design and implementation of this system, are Revisited.
Abstract: Preface. 1. Why Intelligent Hybrid Systems. 2. Methodologies. 3. Intelligent Fusion and Transformation Systems. 4. Intelligent Combination Systems. 5. Knowledge Discovery, Data Mining and Hybrid Systems. 6. Association Systems - Task Structure Level Associative Hybrid Architecture. 7. Intelligent Multi-Agent Hybrid Computational Architecture - Part I. 8. Intelligent Multi-Agent Hybrid Computational Architecture - Part II. 9. Alarm Processing - An Application of IMAHDA. 10. Agent Oriented Analysis and Design of the RTAPS - Part I. 11. Agent Oriented Analysis and Design of the RTAPS - Part II. 12. RTAPS Implementation. 13. From Data Repositories to Knowledge Repositories. 14. IMAHDA Revisited. Appendices: A. Input Features of the Animal Domain. B. Classes in the Animal Domain. C. TTS Power Network. D. TTS Substation Power Network. E. Real Time Alarm Data. Index.

105 citations

Journal ArticleDOI
TL;DR: This paper designs provably correct and spatially distributed algorithms that allow a team of agents to compute a convex and equitable partition of a conveX environment and illustrates a systematic approach to devise spatially distributing control policies for a large variety of multiagent coordination problems.
Abstract: A widely applied strategy for workload sharing is to equalize the workload assigned to each resource. In mobile multiagent systems, this principle directly leads to equitable partitioning policies whereby: 1) the environment is equitably divided into subregions of equal measure; 2) one agent is assigned to each subregion; and 3) each agent is responsible for service requests originating within its own subregion. The current lack of distributed algorithms for the computation of equitable partitions limits the applicability of equitable partitioning policies to limited-size multiagent systems operating in known, static environments. In this paper, first we design provably correct and spatially distributed algorithms that allow a team of agents to compute a convex and equitable partition of a convex environment. Second, we discuss how these algorithms can be extended so that a team of agents can compute, in a spatially distributed fashion, convex and equitable partitions with additional features, e.g., equitable and median Voronoi diagrams. Finally, we discuss two application domains for our algorithms, namely dynamic vehicle routing for mobile robotic networks and wireless ad hoc networks. Through these examples, we show how one can couple the algorithms presented in this paper with equitable partitioning policies to make these amenable to distributed implementation. More in general, we illustrate a systematic approach to devise spatially distributed control policies for a large variety of multiagent coordination problems. Our approach is related to the classic Lloyd algorithm and exploits the unique features of power diagrams.

105 citations

Journal ArticleDOI
TL;DR: A novel Takagi-Sukeno (T-S) fuzzy modeling method is proposed to describe the problem of nonlinear follower agents approaching a time-varying leader and the error dynamics between the follower agents and the leader, whose dynamics is evolving according to an isolated unforced nonlinear agent model is described as a set of T-S fuzzy models.
Abstract: This paper investigates the H∞ consensus control problem of nonlinear multiagent systems under an arbitrary topological structure. A novel Takagi-Sukeno (T-S) fuzzy modeling method is proposed to describe the problem of nonlinear follower agents approaching a time-varying leader, i.e., the error dynamics between the follower agents and the leader, whose dynamics is evolving according to an isolated unforced nonlinear agent model, is described as a set of T-S fuzzy models. Based on the model, a leader-following consensus algorithm is designed so that, under an arbitrary network topology, all the follower agents reach consensus with the leader subject to external disturbances, preserving a guaranteed H∞ performance level. In addition, we obtain a sufficient condition for choosing the pinned nodes to make the entire multiagent network reach consensus. Moreover, the fuzzy modeling method is extended to solve the synchronization problem of nonlinear systems, and a fuzzy H∞ controller is designed so that two nonlinear systems reach synchronization with a prescribed H∞ performance level. The controller design procedure is greatly simplified by utilization of the proposed fuzzy modeling method. Finally, numerical simulations on chaotic systems and arbitrary nonlinear functions are provided to illustrate the effectiveness of the obtained theoretical results.

105 citations

Book ChapterDOI
TL;DR: An Electronic Market architecture implemented through a Multi-Agent system that includes both a specific market agent which plays the role of market coordinator, as well as agents representing the individual business partners with their own goals and strategies is proposed.
Abstract: Electronic Commerce technology has changed the way traditional business is being done. Transactions' complexity is increased due both to the huge amount of available information and also to the environment dynamics. Moreover, Electronic Commerce has enabled the arising of new economical structures, as it is the case of Virtual Organisations. Our research aims at providing flexible and general-purpose systems for intelligent negotiation, both for Electronic Commerce and Virtual Organisation formation. This paper proposes an Electronic Market architecture implemented through a Multi-Agent system. This architecture includes both a specific market agent which plays the role of market coordinator, as well as agents representing the individual business partners with their own goals and strategies. We also include a sophisticated negotiation protocol through multi-criteria and distributed constraint formalisms. An online, continuous reinforcement learning algorithm has been designed to enable agents to adapt themselves according to the changing environment, including the competitor agents.

105 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023536
20221,212
2021849
20201,098
20191,079
20181,105