0000000001262978
AUTHOR
Jon Vegard Jansen
AIs for Dominion Using Monte-Carlo Tree Search
Dominion is a complex game, with hidden information and stochastic elements. This makes creating any artificial intelligence AI challenging. To this date, there is little work in the literature on AI for Dominion, and existing solutions rely upon carefully tuned finite-state solutions. This paper presents two novel AIs for Dominion based on Monte-Carlo Tree Search MCTS methods. This is achieved by employing Upper Confidence Bounds UCB and Upper Confidence Bounds applied to Trees UCT. The proposed solutions are notably better than existing work. The strongest proposal is able to win 67% of games played against a known, good finite-state solution, even when the finite-state solution has the u…
An AI for dominion based on Monte-Carlo methods
Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014 To the best of our knowledge there exists no Arti_cial Intelligence (AI)for Dominion which uses Monte Carlo methods, that is competitive on ahuman level. This thesis presents such an AI, and tests it against someof the top Dominion strategies available. Although in a limited testingenvironment, the results show that our AI is capable of competing withhuman players, while keeping processing time per move at an acceptablelevel for human players. Although the approach for our AI is built onprevious knowledge about Upper Con_dence Bounds (UCB) and UCBapplied to Trees (UCT), an approach for handling the st…