Abstract
The aim of general game playing (GGP) is to create programs capable of playing a wide range of different games at an expert level, given only the rules of the game. The most successful GGP programs currently employ simulation-based Monte Carlo tree search (MCTS). The performance of MCTS depends heavily on the simulation strategy used. In this paper, we investigate the application of a decay factor for two domain-independent simulation strategies: the N-gram selection technique (NST) and themove-average sampling technique (MAST). Three decay factor methods, called move decay, batch decay, and simulation decay, are applied. Furthermore, a combination of move decay and simulation decay is also tested. The decay variants are implemented in the GGP program CADIAPLAYER. Four types of games are used: turn taking, simultaneous move, one player, and multiplayer. Except for one-player games, experiments show that decaying can significantly improve the performance of both NST and MAST simulation strategies.
Original language | English |
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Pages (from-to) | 395-406 |
Journal | IEEE Transactions on Computational Intelligence and AI in Games |
Volume | 6 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2014 |
Keywords
- Decay
- general game playing (GGP)
- Monte Carlo tree search (MCTS)
- N-grams