Adaptive Fighting Game Computer Player by Switching Multiple Rule-based Controllers

This paper proposes the design of a computer player for fighting games that has the advantages of both rule-based and online machine learning players. This method combines multiple computer players as game controllers and switches them at regular time intervals. In this way the computer player as a whole tries to act advantageously against the current opponent player. To select appropriate controllers against the opponent out of the multiple controllers, we use the Sliding Window Upper Confidence Bound (SW-UCB) algorithm that is designed for non-stationary multi-armed bandit problems. We use the FightingICE platform as a testbed for our proposed method. Some experiments show the effectiveness of our proposed method in fighting games. The computer player consists of 3 rule-based computer players, and our method outperforms each of the 3 players. Additionally the proposed method improves the performance a little bit against an online machine learning player

Application of Retrograde Analysis to Fighting Games

With the advent of the fighting game AI competition, there has been recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this thesis we study the design of two-player fighting games and the consequences of the game design on the types of AI that should be used for playing the game, as well as formally define the state space that fighting games are based on. Additionally, we also characterize how AI can solve the game given a simultaneous action game model, to understand the characteristics of the solved AI and the impact it has on game design