Author ORCID Identifier
Thesis - Open Access
Bachelor of Arts
Artificial intelligence, General game playing, General AI, Game theory, Machine learning
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.
Banda, Brandon Mathewe, "General Game Playing as a Bandit-Arms Problem: A Multiagent Monte-Carlo Solution Exploiting Nash Equilibria" (2019). Honors Papers. 116.