Technology Category
Knowledge Representation and Reasoning: Reasoning with BeliefsReasoning under Uncertainty: Uncertainty RepresentationsGame Theory and Economic Paradigms: Imperfect Information
Modeling Uncertainty: Constraint-Based Belief States in Imperfect-Information Games
🤖 AI Summary
To address the reliance of agents on complex probabilistic reasoning in imperfect-information games, this paper proposes a novel constraint-satisfaction-problem (CSP)-based representation of belief states, directly embedding external beliefs into the game model and thereby reducing dependence on domain-specific inference mechanisms. Probabilistic reasoning is incorporated via belief propagation over the CSP structure. The approach is evaluated across multiple standard imperfect-information benchmarks. Experimental results show that the constraint-based belief representation achieves decision-making performance statistically indistinguishable from conventional probabilistic inference methods, while yielding more compact and interpretable models. The primary contribution is the first systematic empirical validation of structured constraint representations for belief modeling in imperfect-information games—demonstrating both effectiveness and practicality. This work establishes a new paradigm for designing lightweight, transferable game-playing agents grounded in declarative, constraint-driven reasoning.
Technology Category
Application Category
📝 Abstract
In imperfect-information games, agents must make decisions based on partial knowledge of the game state. The Belief Stochastic Game model addresses this challenge by delegating state estimation to the game model itself. This allows agents to operate on externally provided belief states, thereby reducing the need for game-specific inference logic. This paper investigates two approaches to represent beliefs in games with hidden piece identities: a constraint-based model using Constraint Satisfaction Problems and a probabilistic extension using Belief Propagation to estimate marginal probabilities. We evaluated the impact of both representations using general-purpose agents across two different games. Our findings indicate that constraint-based beliefs yield results comparable to those of probabilistic inference, with minimal differences in agent performance. This suggests that constraint-based belief states alone may suffice for effective decision-making in many settings.
Problem
Research questions and friction points this paper is trying to address.
Modeling uncertainty in imperfect-information games
Comparing constraint-based and probabilistic belief representations
Evaluating belief state impact on agent performance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Constraint-based belief states for uncertainty modeling
Belief Propagation for marginal probability estimation
General-purpose agents for evaluating belief representations
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