Topic
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.
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TL;DR: This paper defines several successively more sophisticated and epistemically satisfying declarative semantics for agent programs and shows that agent programs cleanly extend well understood semantics for logic programs, and thus are clearly linked to existing results on logic programming and nonmonotonic reasoning.
106 citations
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TL;DR: By analyzing the interactive mode of different dynamic agents, two kinds of effective consensus protocols are proposed for the hybrid multi-agent system which is composed of continuous-time and discrete-time dynamic agents.
106 citations
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28 Aug 1993TL;DR: In this paper, two reinforcement learning algorithms, ACE and AGE, are proposed for the reinforcement learning of appropriate sequences of action sets in multi-agent systems, and experimental results illustrate the learning abilities of these algorithms.
Abstract: This paper deals with learning in reactive multi-agent systems. The central problem addressed is how several agents can collectively learn to coordinate their actions such that they solve a given environmental task together. In approaching this problem, two important constraints have to be taken into consideration: the incompatibility constraint, that is, the fact that different actions may be mutually exclusive; and the local information constraint, that is, the fact that each agent typically knows only a fraction of its environment.
The contents of the paper is as follows. First, the topic of learning in multi-agent systems is motivated (section 1). Then, two algorithms called ACE and AGE (standing for "ACtion Estimation" and "Action Group Estimation", respectively) for the reinforcement learning of appropriate sequences of action sets in multi agent systems are described (section 2). Next, experimental results illustrating the learning abilities of these algorithms are presented (section 3). Finally, the algorithms are discussed and an outlook on future research is provided (section 4).
106 citations
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01 Nov 2008
TL;DR: A new approach to decide on ordering policies of supply chain members in an integrated manner is described and results show that the reinforcement learning ordering mechanism (RLOM) is better than two other known algorithms.
Abstract: A major challenge in supply chain ordering management is the coordination of ordering policies adopted by each level of the chain, so as to minimize inventory costs. This paper describes a new approach to decide on ordering policies of supply chain members in an integrated manner. In the first step supply chain ordering management has been considered as a multi-agent system and formulated as a reinforcement learning (RL) model. In the final step a Q-learning algorithm is proposed to solve the RL model. Results show that the reinforcement learning ordering mechanism (RLOM) is better than two other known algorithms.
106 citations
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01 Jul 2008TL;DR: A multiagent solution to aircraft conflict resolution based on satisficing game theory is described and it is shown that the satisficing approach results in behavior that is attractive both in terms of safety and performance.
Abstract: Future generations of air traffic management systems may give appropriately equipped aircraft the freedom to change flight paths in real time. This would require a conflict avoidance and resolution scheme that is both decentralized and cooperative. We describe a multiagent solution to aircraft conflict resolution based on satisficing game theory. A key feature of the theory is that satisficing decision makers form their preferences by taking into consideration the preferences of others, unlike conventional game theory that models agents that maximize self-interest metrics. This makes possible situational altruism, a sophisticated form of unselfish behavior in which the preferences of another agent are accommodated provided that the other agent will actually take advantage of the sacrifice. This approach also makes possible the creation of groups in which every decision maker receives due consideration. We present simulation results from a variety of scenarios in which the aircraft are limited to constant-speed heading-change maneuvers to avoid conflicts. We show that the satisficing approach results in behavior that is attractive both in terms of safety and performance. The results underscore the applicability of satisficing game theory to multiagent problems in which self-interested participants are inclined to cooperation.
106 citations