Friendly Partner Agent of Poker Game

This paper aims at the construction of a friendly system, which plays a seven-card stud poker game with a human player against an opponent player. This paper calls a cooperative friendly system a partner agent. Seven-card stud poker is a kind of a game with imperfect information and it is difficult to consider optimal strategy in such a game. Therefore, the partner agent has a discussion with a human player on game strategy cooperating with the player. If a human player needs some advice on a game, the partner agent presents its reply. Furthermore, the partner agent presents not only linguistic expressions of strategy but also facial expressions according to the game situation so that a human player can feel a sense of affinity with the agent. Neural network models are used for facial expressions. This paper also performs subject experiments to evaluate the effectiveness of the proposed partner agent with facial expressions, where each subject plays poker games with partner agents. This paper analyzes questionnaire data that subject answer after experiments.