Application of Retrograde Analysis to Fighting Games

<p>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.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2019

Author dc:creator

• Yu, Kristen

Contributors dc:contributor

• Nathan R. Sturtevant, Ph.D.

• Artificial intelligence
• Fighting game artificial intelligence
• Reinforcement learning
• Retrograde analysis
• Artificial Intelligence and Robotics
• Computer Sciences
• Game Design
• Physical Sciences and Mathematics

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/1633

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-2633