Georgios Chalkiadakis
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: gehalk@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Research on coalition formation usually assumes the
values of potential coalitions to be known with certainty.
Furthermore, settings in which agents lack sufficient knowledge of
the capabilities of potential partners is rarely, if ever, touched upon.
We remove these often unrealistic assumptions and
propose a model that utilizes Bayesian (multiagent) reinforcement learning
in a way that enables coalition participants to reduce
their uncertainty regarding
coalitional values and the capabilities of others.
In addition, we introduce the Bayesian Core, a new stability concept
for coalition formation under uncertainty.
Preliminary experimental evidence demonstrates
the effectiveness of our approach.
To appear, AAMAS-04
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