|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A hyper-heuristic methodology to generate adaptive strategies for games|
iterated prisoner’s dilemma
|Citation:||Li J & Kendall G (2017) A hyper-heuristic methodology to generate adaptive strategies for games, IEEE Transactions on Computational Intelligence and AI in Games, 9 (1), pp. 1-10.|
|Abstract:||Hyper-heuristics have been successfully applied in solving a variety of computational search problems. In this study, we investigate a hyper-heuristic methodology 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. As examples, we develop hyper-heuristic game players for three games: iterated prisoner’s dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic 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.|
|Rights:||This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/|
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