Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Bargaining over multiple issues in finite horizon alternating-offers protocol
Annals of Mathematics and Artificial Intelligence
Stochastic search methods for nash equilibrium approximation in simulation-based games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Avoiding the prisoner's dilemma in auction-based negotiations for highly rugged utility spaces
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Adaptive search with stochastic acceptance probabilities for global optimization
Operations Research Letters
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The class of simulation--based games, in which the payoffs are generated as an output of a simulation process, recently received a lot of attention in literature. In this paper, we extend such class to games in extensive form with continuous actions and perfect information. We design two convergent algorithms to find an approximate subgame perfect equilibrium (SPE) and an approximate Nash equilibrium (NE) respectively. Our algorithms can exploit different optimization techniques. In particular, we use: simulated annealing, cross entropy method, and Lipschitz optimization. We produce an extensive experimental evaluation of the performance of our algorithms in terms of approximation degree of the optimal solution and number of evaluated samples. Finding approximate NE and SPE requires exponential time in the game tree depth: an SPE can be computed in game trees with a small depth, while the computation of an NE is easier.