Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Abstract
There has been a growing interest in AI in the design of
multiagent systems, especially in multiagent cooperative
planning. In this paper, we investigate the extent to which
methods from single-agent planning and learning can be applied
in multiagent settings. We survey a number of different techniques
from decision-theoretic planning and reinforcement learning and
describe a number of interesting issues that arise with regard to
coordinating the policies of individual agents. To this end, we
describe multiagent Markov decision processes as a general
model in which to frame this discussion. These are special n-person
cooperative games in which agents share the same utility function.
We discuss coordination mechanisms based on imposed conventions
(or social laws) as well as learning methods for coordination.
Our focus is on the decomposition of sequential decision processes
so that coordination can be learned (or imposed) locally, at the
level of individual states. We also discuss the use of structured
problem representations and their role in the generalization
of learned conventions and in approximation.
To appear, TARK-96
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