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Optimizing strategy selection in hidden role games

delete2025-10-10
delete0
PRE
AI
Y
Yingying Xu
C
Chen Qiu
J
Jinheng Xiao
J
Jiajia Zhang
S
Shuhan Qi
王
王晅 (Xuan Wang) *
DOI：10.1016/j.engappai.2025.112464delete
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Abstract

Abstract

En 中文
We address hidden-role decision making under uncertainty in The Resistance: Avalon. We present DeepBayes, which augments a standard Counterfactual Regret Minimization Plus (CFR+) decision procedure with two complementary inference components. First, a history-driven role assignment prediction network generates role-assignment hypotheses from past gameplay, which are used to improve the estimation of Counterfactual Values (CFVs). Second, a Bayesian Identity Recognition (BIR) method produces explicit posterior beliefs about opposing identities online as play unfolds. During CFR+ iterations, the algorithm selects actions by jointly considering the CFVs estimated under the generated role assignments and the posterior beliefs from BIR. In five-player Avalon experiments, DeepBayes achieves consistent gains in win rate over strong baselines.

Journal

Engineering Applications of Artificial Intelligence cover
Engineering Applications of Artificial Intelligence
IF:
8
Papers:
5.3K
Citations:
3.5W

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