Abstract
Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic 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.
Original language | English |
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Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | IEEE Transactions on Computational Intelligence and AI in Games |
Volume | 9 |
Issue number | 1 |
DOIs | |
Publication status | Published - Mar 2017 |
Keywords
- Competitive traveling salesmen problem
- Goofspiel
- game
- hyperheuristic
- iterated prisoner's dilemma (IPD)
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering