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.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5582 |
| Date | 01 May 2022 |
| Creators | Gillenwater, Jacob |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
Page generated in 0.0014 seconds