| Abstract |
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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.
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 Additional Information
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Citation:
Georgios Chalkiadakis, Craig Boutilier,
"Bayesian Reinforcement Learning for Coalition Formation under Uncertainty,"
aamas,
pp. 1090-1097,
Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04),
2004
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