Publication: Correcting and improving imitation models of humans for Robosoccer agents
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2005-09
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IEEE
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Abstract
The Robosoccer simulator is a challenging environment, where a human introduces a team of agents into a football virtual environment. Typically, agents are programmed by hand, but it would be a great advantage to transfer human experience into football agents. The first aim of this paper is to use machine learning techniques to obtain models of humans playing Robosoccer. These models can be used later to control a Robosoccer agent. However, models did not play as smoothly and optimally as the human. To solve this problem, the second goal of this paper is to incrementally correct models by means of evolutionary techniques, and to adapt them against more difficult opponents than the ones beatable by the human.
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Proceeding of: 2005 IEEE Congress on Evolutionary Computation (CEC'05),Edimburgo, 2-5 Sept. 2005
Keywords
Digital simulation, Evolutionary computation, Learning (artificial intelligence), Multi-agent systems, Multi-robot systems
Bibliographic citation
2005 IEEE Congress on Evolutionary Computation (CEC05), vol. 3, p. 2402 - 2409