Deduction Game Framework and Information Set Entropy Search

Autor:	Meng, Fandi, Lucas, Simon
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: IEEE Conference on Games (IEEE CoG)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.21178 Zobrazit plný text záznamu View this record from Arxiv