Degree Name
MS (Master of Science)
Program
Computer and Information Sciences
Date of Award
5-2022
Committee Chair or Co-Chairs
Brian Bennett
Committee Members
Ghaith Husari, Jeff Roach
Abstract
Hasbro’s RISK, first published in 1959, is a complex multiplayer strategy game that has received little attention from the scientific community. Training artificial intelligence (AI) agents using stochastic beam search gives insight into effective strategy when playing RISK. A comprehensive analysis of the systems of play challenges preconceptions about good strategy in some areas of the game while reinforcing those preconceptions in others. This study applies stochastic beam search to discover optimal strategies in RISK. Results of the search show both support for and challenges to traditionally held positions about RISK gameplay. While stochastic beam search competently investigates gameplay on a turn-by-turn basis, the search cannot create contingencies that allow for effective strategy across multiple turns. Future work would investigate additional algorithms that eliminate this limitation to provide further insights into optimal gameplay strategies.
Document Type
Thesis - embargo
Recommended Citation
Gillenwater, Jacob, "RISK Gameplay Analysis Using Stochastic Beam Search" (2022). Electronic Theses and Dissertations. Paper 4063. https://dc.etsu.edu/etd/4063
Copyright
Copyright by the authors.
Included in
Artificial Intelligence and Robotics Commons, Software Engineering Commons, Theory and Algorithms Commons