Exploiting Structure in Cooperative Bayesian Games
This work addresses scalability issues in multi-agent decision-making under uncertainty, representing an incremental advance by extending independence concepts from perfect to imperfect information settings.
The paper tackles the computational intractability of cooperative Bayesian games by introducing a factor graph representation that exploits type independence, enabling the solution of games of unprecedented size.
Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings, we propose a novel approach for BGs based on the observation that BGs additionally possess a different types of structure, which we call type independence. We propose a factor graph representation that captures both forms of independence and present a theoretical analysis showing that non-serial dynamic programming cannot effectively exploit type independence, while Max-Sum can. Experimental results demonstrate that our approach can tackle cooperative Bayesian games of unprecedented size.