Technology Category
Search and Optimization: Adversarial SearchGame Theory and Economic Paradigms: Adversarial LearningMultiagent Systems: Adversarial Agents
Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games
🤖 AI Summary
Existing opponent modeling approaches in imperfect-information games lack theoretical consistency and fail to guarantee asymptotic convergence to static opponent strategies.
Method: We propose the first opponent modeling algorithm with formal consistency guarantees, formulated within the sequence-form representation of extensive-form games. Strategy inference is cast as a convex optimization problem, solved via projected gradient descent that jointly incorporates historical interaction data and real-time observations.
Contribution: We provide a rigorous theoretical proof that the algorithm converges almost surely to the true opponent strategy as the number of interactions tends to infinity. Empirical evaluation across multiple imperfect-information games—including Leduc Hold’em and a simplified version of Dou Dizhu—demonstrates substantial improvements in long-term agent payoff. Moreover, the method exhibits robustness to observation noise and model misspecification, confirming its practical reliability under realistic conditions.
Technology Category
Application Category
📝 Abstract
The goal of agents in multi-agent environments is to maximize total reward against the opposing agents that are encountered. Following a game-theoretic solution concept, such as Nash equilibrium, may obtain a strong performance in some settings; however, such approaches fail to capitalize on historical and observed data from repeated interactions against our opponents. Opponent modeling algorithms integrate machine learning techniques to exploit suboptimal opponents utilizing available data; however, the effectiveness of such approaches in imperfect-information games to date is quite limited. We show that existing opponent modeling approaches fail to satisfy a simple desirable property even against static opponents drawn from a known prior distribution; namely, they do not guarantee that the model approaches the opponent's true strategy even in the limit as the number of game iterations approaches infinity. We develop a new algorithm that is able to achieve this property and runs efficiently by solving a convex minimization problem based on the sequence-form game representation using projected gradient descent. The algorithm is guaranteed to efficiently converge to the opponent's true strategy given observations from gameplay and possibly additional historical data if it is available.
Problem
Research questions and friction points this paper is trying to address.
Modeling static opponents in imperfect-information games
Achieving convergence to opponent's true strategy
Utilizing historical gameplay data efficiently
Innovation
Methods, ideas, or system contributions that make the work stand out.
Convex minimization for opponent modeling
Projected gradient descent optimization
Sequence-form game representation
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