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Learning collaboration strategies for committees of learning agents

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Abstract

A main issue in cooperation in multi-agent systems is how an agent decides in which situations is better to cooperate with other agents, and with which agents does the agent cooperate. Specifically in this paper we focus on multi-agent systems composed of learning agents, where the goal of the agents is to achieve a high accuracy on predicting the correct solution of the problems they encounter. For that purpose, when encountering a new problem each agent has to decide whether to solve it individually or to ask other agents for collaboration. We will see that learning agents can collaborate forming committees in order to improve performance. Moreover, in this paper we will present a proactive learning approach that will allow the agents to learn when to convene a committee and with which agents to invite to join the committee. Our experiments show that learning results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy than forming committees composed of all agents.

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  1. Santiago Ontañón

    Present address: MAiA – Department of Applied Mathematics, University of Barcelona, Gran Via 585, 08007, Barcelona, Catalonia, Spain

Authors and Affiliations

  1. IIIA – Artificial Intelligence Research Institute, CSIC – Spanish Counsel for Scientific Research, Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza & Santiago Ontañón

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  1. Enric Plaza
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  2. Santiago Ontañón
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Correspondence to Santiago Ontañón.

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Plaza, E., Ontañón, S. Learning collaboration strategies for committees of learning agents. Auton Agent Multi-Agent Syst 13, 429–461 (2006). https://doi.org/10.1007/s10458-006-0015-x

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