Abstract
One important class of problems in Multi-Agent Systems (MASs) is planning, that is constructing an optimal policy for each agent with the objective of reaching some terminal goal state. The key aspect of multi-agent planning is coordinating the actions of the individual agents. This coordination may be done through communication, learning, or conventions imposed at design time. In this paper we present a new taxonomy of MASs that is based on the notions of optimality and rationality. A framework that describes the interactions between the agents and their environment is given, along with a reinforcement learning-based algorithm (Q-learning) for learning a joint optimal plan. Finally, we give some experimental results on grid games that show the convergence of this algorithm.
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© 2003 Springer-Verlag Berlin Heidelberg
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Gomaa, W.E., Saad, A.A., Ismail, M.A. (2003). Learning Joint Coordinated Plans in Multi-agent Systems. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_16
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DOI: https://doi.org/10.1007/3-540-45034-3_16
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