Summary of Deduction Game Framework and Information Set Entropy Search, by Fandi Meng and Simon Lucas
Deduction Game Framework and Information Set Entropy Search
by Fandi Meng, Simon Lucas
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed game framework is designed for deduction games, allowing for structured analysis through Shannon entropy variations. A new forward search algorithm, Information Set Entropy Search (ISES), is introduced, which efficiently solves many single-player deduction games. The ISES algorithm, combined with sampling techniques, enables agents to make decisions within controlled computational resources and time constraints. Experimental results on eight games demonstrate the superiority of our method over Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) under limited decision time constraints. The framework’s entropy variation also enables explainable decision-making, providing insights for game designers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new game-playing system is developed that can solve many deduction games quickly and efficiently. This system uses a clever algorithm called ISES, which helps make decisions by considering the uncertainty of different game states. The algorithm works well even when there are time constraints or limited computer resources. In experiments, the system outperformed another popular approach on eight different games. The system’s ability to analyze game states also helps explain why people find some deduction games appealing and provides insights for game designers. |
