Author ORCID Identifier
Degree Year
2019
Document Type
Thesis - Open Access
Degree Name
Bachelor of Arts
Department
Computer Science
Advisor(s)
Robert Geitz
Keywords
Artificial intelligence, General game playing, General AI, Game theory, Machine learning
Abstract
This project approaches general game playing in a unique way by combining popular methods of stochastic tree searching with a Multiagent system and a unique algorithm that I call the Wise Explorer algorithm. The goal of the system is to explore the worst possible branches of the game first to rule them out, followed by an in-depth search on the most promising branches. The system constantly refers to the data it collects during its extensive search, and it outputs a strategic move for any given state of a game. In essence, if you’re ever in a bind during a game of tic-tac-toe, the system will tell you exactly what your best move is.
Repository Citation
Banda, Brandon Mathewe, "General Game Playing as a Bandit-Arms Problem: A Multiagent Monte-Carlo Solution Exploiting Nash Equilibria" (2019). Honors Papers. 116.
https://digitalcommons.oberlin.edu/honors/116
