Mimicking Human Strategies in Fighting Games using a Data Driven Finite State Machine
MuKiplayer fighting videogames · have become an increasingly popular over the last few years, especially with the introduction of online play, making, for a more competitive experience. Multiplayer fighting games give players the opportunity to utilize particular strategies and tactics to win, allowing them to use their own signature style. As a player can only play against a particular opponent who is actively participating in the game themselves, they cannot practice combating the opponents style if the opponent is not participating in the game. This paper presents a novel approach for an avatar to learn and mimic the style of a player. It does this by recording and analyzing the data before splitting it up into two tiers; tactical data and strategic data.. Theapproach uses a Naive Bayes classifier to classify the tactics to particular states, and a Data Driven Finite State Machine to dictate when certain tactics are used. Statistics recorded during an experiment involving the approach are discussed, which indicate that the architecture of the Artificial Intelligence is fit for purpose, but does require refinement. Limitations of the architecture are discussed, including that such an approach may not provide accurate results when more parameters are considered.
Artificial Intelligence fighting game FSM Bayes
S. Saini P.W.H. Chung C. W. Dawson
Department of Computer Science,Loughborough University,Loughborough, UK, LE11 3TU
国际会议
重庆
英文
892-896
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）