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Adversarial Decision-Making in Partially Observable Multiagent Systems: A Sequential Hypothesis Testing Approach
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DOI：10.1109/tcns.2026.3690569.png)
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Abstract
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Adversarial decision-making in partially observable multiagent systems requires sophisticated strategies for both deception and counter-deception. This article presents a sequential hypothesis testing-driven framework that captures the interplay between strategic misdirection and inference in adversarial environments. We formulate this interaction as a partially observable Stackelberg game, where a follower agent (blue team) seeks to fulfill its primary task while actively misleading an adversarial leader (red team). In opposition, the red team, leveraging leaked information, instills carefully designed patterns to manipulate the blue team's behavior, mitigating the misdirection effect. Unlike conventional approaches that focus on robust control under adversarial uncertainty, our framework explicitly models deception as a dynamic optimization problem, where both agents strategically adapt their policies in response to inference and counter-inference. We derive a semiexplicit optimal control solution for the blue team within a linear–quadratic setting and develop iterative and machine learning-based methods to characterize the red team's optimal response. Numerical experiments demonstrate how deception-driven strategies influence adversarial interactions and reveal the impact of leaked information in shaping equilibrium behaviors. These results provide new insights into strategic deception in multiagent systems, with potential applications in cybersecurity, autonomous decision-making, and financial markets.
Keywords:
Adversarial decision-making
partially observable games
sequential hypothesis testing (SHT)
Stackelberg games
stochastic optimal control
strategic deception and counter-deception
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