In Social Deduction Games (SDGs) such as Avalon, Mafia, and Werewolf, players conceal their identities and deliberately mislead others, making hidden-role inference a central and demanding task. Accurate role identification, which forms the basis of an agent's belief state, is therefore the keystone for both human and AI performance. We introduce CSP4SDG, a probabilistic, constraint–satisfaction framework that analyses gameplay objectively. Game events and dialogue are mapped to four linguistically agnostic constraint classes—evidence, phenomena, assertions, and hypotheses. Hard constraints prune impossible role assignments, while weighted soft constraints score the remainder; information-gain weighting links each hypothesis to its expected value under entropy reduction, and a simple closed-form scoring rule guarantees that truthful assertions converge to classical hard logic with minimum error. The resulting posterior over roles is fully interpretable and updates in real time. Experiments on three public datasets show that CSP4SDG (i) outperforms LLM-based baselines in every inference scenario, and (ii) boosts LLMs when supplied as an auxiliary "reasoning tool." Our study validates that principled probabilistic reasoning with information theory is a scalable alternative—or complement—to heavy-weight neural models for SDGs.

This demo paper introduces a novel interactive system where human players control AI companions using voice commands in a cooperative survival game. Players direct their AI companions through natural language, enhancing gameplay through intuitive interaction. The preliminary demo implementation includes basic behavior management, allowing AI agents to combat enemies, gather resources, and execute player commands. Future versions will further enrich interaction possibilities and AI autonomy levels, serving as a platform to explore advanced human-AI collaboration dynamics.

Single-player deduction games are a canonical form of hidden-information reasoning. Agents iteratively issue actions (queries) and receive deterministic feedback, thereby shrinking the information set of feasible secret codes. Classical search techniques-such as Information-Set Monte-Carlo Tree Search (ISMCTS) or the entropy-driven Information-Set Entropy Search (ISES)-handle these games by sampling or by fully enumerating states, but both methods encounter difficulties when the combinatorial space explodes. This paper introduces a constraintpropagation variant of ISES that models the information set as a constraint-satisfaction problem (CSP) and applies the AC-3 arcconsistency algorithm after every observation. By aggressively pruning unsupported variable values before entropy evaluation, the method eliminates a large number of impossible states and accelerates inference without sacrificing optimality. Using several single-player deduction games from the Deduction Game Framework as case studies, we show that constraint propagation significantly enhances the efficiency of ISES.

This study investigates efficient AI agents for the multi-player deduction game Cluedo.We propose a knowledge representation method based on constraint satisfaction problems (CSPs) and formalize the deductive reasoning process into two key modules: knowledge updating and action selection, enabling the creation of multiple distinct AI agents.Experimental results demonstrate that constraint-solving methods can be effectively applied to build powerful Cluedo AI agents.

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.