Efficient Coalition Structure Generation via Approximately Equivalent Induced Subgraph Games

Filippo Bistaffa,Georgios Chalkiadakis,Alessandro Farinelli

IEEE Transactions on Cybernetics（2022）

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摘要

We show that any characteristic function game (CFG)

$G$

can be always turned into an approximately equivalent game represented using the induced subgraph game (ISG) representation. Such a transformation incurs obvious benefits in terms of tractability of computing solution concepts for

$G$

. Our transformation approach, namely, AE-ISG, is based on the solution of a norm approximation problem. We then propose a novel coalition structure generation (CSG) approach for ISGs that is based on graph clustering, which outperforms existing CSG approaches for ISGs by using off-the-shelf optimization solvers. Finally, we provide theoretical guarantees on the value of the optimal CSG solution of

$G$

with respect to the optimal CSG solution of the approximately equivalent ISG. As a consequence, our approach allows one to compute approximate CSG solutions with quality guarantees for any CFG. Results on a real-world application domain show that our approach outperforms a domain-specific CSG algorithm, both in terms of quality of the solutions and theoretical quality guarantees.

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关键词

Algorithms,Cluster Analysis

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