Affiliation:
1. Huawei Technologies Ltd
2. Greyc, University of Caen - Normandy
Abstract
Constraint Games are a recent framework proposed to model and solve static games where Constraint Programming is used to express players preferences. In this paper, we rethink their solving technique in terms of constraint propagation by considering players preferences as global constraints. It yields not only a more elegant but also a more efficient framework. Our new complete solver is faster than previous state-of-the-art and is able to find all pure Nash equilibria for some problems with 200 players. We also show that performances can greatly be improved for graphical games, allowing some games with 2000 players to be solved.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
5 articles.
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