Introduction to Behavior Algorithms for Fighting Games
This work addresses the need for improved AI in fighting games to support the growing market for both casual players and tournaments, but it is incremental as it primarily reviews and combines existing algorithms.
The paper introduces standard and recent behavior algorithms for video games, such as Finite-State Machines, Behavior Trees, and Monte-Carlo Tree Search, and discusses their potential applications in fighting games to enhance opponent AI, which is crucial for the adversarial experience in this genre.
The quality of opponent Artificial Intelligence (AI) in fighting videogames is crucial. Some other game genres can rely on their story or visuals, but fighting games are all about the adversarial experience. In this paper, we will introduce standard behavior algorithms in videogames, such as Finite-State Machines and Behavior Trees, as well as more recent developments, such as Monte-Carlo Tree Search. We will also discuss the existing and potential combinations of these algorithms, and how they might be used in fighting games. Since we are at the financial peak of fighting games, both for casual players and in tournaments, it is important to build and expand on fighting game AI, as it is one of the pillars of this growing market.