Conference proceeding
Single believe state generation for partially observable real-time strategy games
2017 IEEE Conference on Computational Intelligence and Games (CIG), pp 296-303
Aug 2017
Abstract
Real-Time Strategy (RTS) games pose a big challenge due their large branching factor and real-time nature. This challenge is even bigger if we consider partially observable RTS games due to the fog-of-war. This paper focuses on extending Monte Carlo Tree Search (MCTS) algorithms for RTS games to consider partially observable settings. Specifically, we investigate sampling a single believe state consistent with a perfect memory of all the past observations in the current game, and using it to perform MCTS. We evaluate the performance of this approach in the μRTS game simulator, showing that the performance of this approach is only between 8%-15% lower than if we could observe the entire game state (e.g., by cheating).
Metrics
11 Record Views
10 citations in Scopus
Details
- Title
- Single believe state generation for partially observable real-time strategy games
- Creators
- Alberto Uriarte - Drexel UniversitySantiago Ontanon - Drexel University
- Publication Details
- 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp 296-303
- Conference
- 2017 IEEE Conference on Computational Intelligence and Games (CIG)
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-85040006825
- Other Identifier
- 991019174512204721