Deduction Game Framework and Information Set Entropy Search

TL;DR

This paper introduces a structured deduction game framework based on Shannon entropy and a novel algorithm, ISES, that outperforms existing methods in solving single-player deduction games efficiently and with explainability.

Contribution

It presents a new deduction game framework and the ISES algorithm, which improves decision-making efficiency and explainability in single-player deduction games.

Findings

ISES outperforms SO-ISMCTS under limited decision time.

Entropy variation enables explainable decision-making.

Framework provides insights for game design and analysis.

Abstract

We present a game framework tailored for deduction games, enabling structured analysis from the perspective of Shannon entropy variations. Additionally, we introduce a new forward search algorithm, Information Set Entropy Search (ISES), which effectively solves many single-player deduction games. The ISES algorithm, augmented with sampling techniques, allows agents to make decisions within controlled computational resources and time constraints. Experimental results on eight games within our framework demonstrate the significant superiority of our method over the Single Observer Information Set Monte Carlo Tree Search(SO-ISMCTS) algorithm under limited decision time constraints. The entropy variation of game states in our framework enables explainable decision-making, which can also be used to analyze the appeal of deduction games and provide insights for game designers.