|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A hyper-heuristic methodology to generate adaptive strategies for games (Forthcoming/Available Online)|
iterated prisoner’s dilemma
|Citation:||Li J & Kendall G (2015) A hyper-heuristic methodology to generate adaptive strategies for games (Forthcoming/Available Online), IEEE Transactions on Computational Intelligence and AI in Games.|
|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:||© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Affiliation:||Computing Science - CSM Dept|
University of Nottingham
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