Date of Award
1-1-2019
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
First Advisor
Nathan R. Sturtevant, Ph.D.
Keywords
Artificial intelligence, Fighting game artificial intelligence, Reinforcement learning, Retrograde analysis
Abstract
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.
Copyright Date
January 2019
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Kristen Yu
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
65 p.
Recommended Citation
Yu, Kristen, "Application of Retrograde Analysis to Fighting Games" (2019). Electronic Theses and Dissertations. 1633.
https://digitalcommons.du.edu/etd/1633
Discipline
Artificial intelligence