Learning collaboration strategies for committees of learning agents |
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Authors: | Enric Plaza Santiago Ontañón |
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Affiliation: | (1) IIIA – Artificial Intelligence Research Institute, CSIC – Spanish Counsel for Scientific Research, Campus UAB, 08193 Bellaterra, Catalonia, Spain;(2) Present address: MAiA – Department of Applied Mathematics, University of Barcelona, Gran Via 585, 08007 Barcelona, Catalonia, Spain |
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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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Keywords: | Multi-agent learning Committees Meta learning Case based reasoning |
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