Abstract :
Hyper-heuristics have been successfully applied in solving a variety of computational search problems. We discuss how a hyper-heuristic can be used to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyper-heuristic game player can generate strategies which adapt to both the behaviour of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialised games can be integrated into an automated game player. We have developed hyper-heuristics for three games: iterated prisoner´s dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyper-heuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.
