Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The complexity of computing a Nash equilibrium
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Simple search methods for finding a Nash equilibrium
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mixed-integer programming methods for finding Nash equilibria
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
New results on the verification of Nash refinements for extensive-form games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Finding a nash equilibrium by asynchronous backtracking
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Using representative strategies for finding nash equilibria
Proceedings of the 15th annual conference on Genetic and evolutionary computation
On the verification and computation of strong nash equilibrium
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
| Hi-index | 0.00 |
The computation of a Nash equilibrium in a game is a challenging problem in artificial intelligence. This is because the computational time of the algorithms provided by the literature is, in the worst case, exponential in the size of the game. To deal with this problem, it is common the resort to concepts of approximate equilibrium. In this paper, we follow a different route, presenting, to the best of our knowledge, the first algorithm based on the combination of support enumeration methods and local search techniques to find an exact Nash equilibrium in two-player general-sum games and, in the case no equilibrium is found within a given deadline, to provide an approximate equilibrium. We design some dimensions for our algorithm and we experimentally evaluate them with games that are unsolvable with the algorithms known in the literature within a reasonable time. Our preliminary results are promising, showing that our techniques can allow one to solve hard games in a short time.