Autonomous Agents that Learn to Better Coordinate

Garland, Andrew; Alterman, Richard

doi:10.1023/b:agnt.0000018808.95119.9e

Cited by 15 publications

(8 citation statements)

References 33 publications

(35 reference statements)

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“…A related approach is presented in [37,36]: here, Bayesian learning is used to incrementally update models of other agents to reduce communication load by anticipating their future actions based on their previous ones. Case-based learning has also been used to develop successful joint plans based on one's historical expectations of other agents' actions [83].…”

Section: Teammate Modelingmentioning

confidence: 99%

Cooperative Multi-Agent Learning: The State of the Art

Panait

Luke

2005

Auton Agent Multi-Agent Syst

982

559

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Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to multi-agent systems problems has spawned increasing interest in machine learning techniques to automate the search and optimization process.We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics.We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.

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Section: Teammate Modelingmentioning

confidence: 99%

Cooperative Multi-Agent Learning: The State of the Art

Panait

Luke

2005

Auton Agent Multi-Agent Syst

982

559

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“…In most cases these strategies are based on a central traffic-responsive control system, whose difficulty to implement [18,20,36]). This is a key issue in the traffic domain as it will be shown in Section 3, where other relevant contributions are also briefly discussed.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Approach for Coordination of Traffic Signal Agents

Bazzan

2005

Auton Agent Multi-Agent Syst

180

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Innovative control strategies are needed to cope with the increasing urban traffic chaos. In most cases, the currently used strategies are based on a central traffic-responsive control system which can be demanding to implement and maintain. Therefore, a functional and spatial decentralization is desired. For this purpose, distributed artificial intelligence and multi-agent systems have come out with a series of techniques which allow coordination and cooperation. However, in many cases these are reached by means of communication and centrally controlled coordination processes, giving little room for decentralized management. Consequently, there is a lack of decision-support tools at managerial level (traffic control centers) capable of dealing with decentralized policies of control and actually profiting from them. In the present work a coordination concept is used, which overcomes some disadvantages of the existing methods. This concept makes use of techniques of evolutionary game theory: intersections in an arterial are modeled as individually-motivated agents or players taking part in a dynamic process in which not only their own local goals but also a global one has to be taken into account. The role of the traffic manager is facilitated since s/he has to deal only with tactical ones, leaving the operational issues to the agents. Thus the system ultimately provides support for the traffic manager to decide on traffic control policies. Some application in traffic scenarios are discussed in order to evaluate the feasibility of transferring the responsibility of traffic signal coordination to agents. The results show different performances of the decentralized coordination process in different scenarios (e.g. the flow of vehicles is nearly equal in both opposing directions, one direction has a clearly higher flow, etc.). Therefore, the task of the manager is facilitate once s/he recognizes the scenario and acts accordingly.

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“…We model capabilities probabilistically as a function of the agent, a teammate, and their mutual state. Kok and Vlassis use states to model teammates to coordinate actions [9]; Gmytrasiewicz and Doshi model agents to select optimal actions [6]; Garland and Alterman study conventions to coordinate actions [4]. We model capabilities to select a role assignment to form a team with a high utility, calculating the performance of the role assignment by using the statistics of the capabilities.…”

Section: Related Workmentioning

confidence: 99%

Mutual state-based capabilities for role assignment in heterogeneous teams

Liemhetcharat

Veloso

2010

Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots

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In a heterogeneous team, agents have different capabilities with regards to the actions relevant to the task. Roles are typically assigned to individual agents in such a team, where each role is responsible for a certain aspect of the joint team goal. In this paper, we focus on role assignment in a heterogeneous team, where an agent's capability depends on its teammate and their mutual state, i.e., the agent's state and its teammate's state. The capabilities of an agent are represented by a mean and variance, to capture the uncertainty in the agent's actions as well as the uncertainty in the world. We present a formal framework for representing this problem, and illustrate our framework using a robot soccer example. We formally describe how to compute the value of a role assignment policy, as well as the computation of the optimal role assignment policy, using a notion of risk. Further, we show that finding the optimal role assignment can be difficult, and describe approximation algorithms that can be used to solve this problem. We provide an analysis of these algorithms in our model and empirically show that they perform well in general problems of this domain, compared to market-based techniques.

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Autonomous Agents that Learn to Better Coordinate

Cited by 15 publications

References 33 publications

Cooperative Multi-Agent Learning: The State of the Art

Cooperative Multi-Agent Learning: The State of the Art

A Distributed Approach for Coordination of Traffic Signal Agents

Mutual state-based capabilities for role assignment in heterogeneous teams

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