Using HMM in Strategic Games
This work addresses strategic decision-making in dynamic games for players or AI systems, but it appears incremental as it combines existing methods without claiming major breakthroughs.
The paper tackles the problem of inferring an opponent's changing type in strategic games to improve a player's odds, using a combination of Markov games and hidden Markov models, and demonstrates the approach with a hypothetical tennis game example.
In this paper we describe an approach to resolve strategic games in which players can assume different types along the game. Our goal is to infer which type the opponent is adopting at each moment so that we can increase the player's odds. To achieve that we use Markov games combined with hidden Markov model. We discuss a hypothetical example of a tennis game whose solution can be applied to any game with similar characteristics.