Evaluating Learning Automata as a Model for Cooperation in Complex Multi-agent Domains
نویسندگان
چکیده
Learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multiagent systems, using soccer simulation server as a test bed. We have also evaluated our learning method in hard situations such as malfunctioning of some of the agents in the team and in situations that agents’ sense/act abilities have a lot of noise involved. Our experiment results show that learning automata adapt well with these situations.
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