Introduction to Behavior Algorithms for Fighting Games

TL;DR

This paper reviews standard and recent behavior algorithms like Finite-State Machines, Behavior Trees, and Monte-Carlo Tree Search, discussing their applications and potential in enhancing AI for fighting games.

Contribution

It introduces and compares various behavior algorithms for fighting game AI, highlighting their integration and potential for improving adversarial experience.

Findings

Behavior algorithms are crucial for engaging fighting game AI.

Recent developments like Monte-Carlo Tree Search offer new possibilities.

Combining algorithms can enhance AI performance in fighting games.

Abstract

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.