Evaluating Learning Automata as a Model for Cooperation in Complex Multi-agent Domains

  • Authors:
  • Mohammad Reza Khojasteh;Mohammad Reza Meybodi

  • Affiliations:
  • AI & Robotics Laboratory, Computer Engineering Department, Shiraz Islamic Azad University, Shiraz, Iran;Soft Computing Laboratory, Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

  • Venue:
  • RoboCup 2006: Robot Soccer World Cup X
  • Year:
  • 2006

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Abstract

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 multi-agent 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.