Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
The Development of Embodied Cognition: Six Lessons from Babies
Artificial Life
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Playing for Real: A Text on Game Theory
Playing for Real: A Text on Game Theory
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Artificial Life
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
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Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the min-max solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash.