On the combinatorial and algebraic complexity of quantifier elimination
Journal of the ACM (JACM)
The Advantages of Compromising in Coalition Formation with Incomplete Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Bayesian Reinforcement Learning for Coalition Formation under Uncertainty
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Coalition formation under uncertainty: bargaining equilibria and the Bayesian core stability concept
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Dynamic coalition formation under uncertainty
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ICLA'11 Proceedings of the 4th Indian conference on Logic and its applications
Belief-based stability in non-transferable utility coalition formation with uncertainty
Intelligent Decision Technologies
Sequentially optimal repeated coalition formation under uncertainty
Autonomous Agents and Multi-Agent Systems
Leveraging domain knowledge to learn normative behavior: a bayesian approach
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
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Coalition formation is a problem of great interest in AI, allowing groups of autonomous, individually rational agents to form stable teams. Automating the negotiations underlying coalition formation is, naturally, of special concern. However, research to date in both AI and economics has largely ignored the potential presence of uncertainty in coalitional bargaining. We present a model of discounted coalitional bargaining where agents are uncertain about the types (or capabilities) of potential partners, and hence the value of a coalition. We cast the problem as a Bayesian game in extensive form, and describe its Perfect Bayesian Equilibria as the solutions to a polynomial program. We then present a heuristic algorithm using iterative coalition formation to approximate the optimal solution, and evaluate its performance.