  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.
