This paper addresses a multi-agent target defense differential game in which multiple static targets are protected by visibility-constrained autonomous defenders against a rational attacker. The attacker privately selects a fixed intended target from the set of static targets, while defenders experience intermittent access to the attacker’s state due to periodic sensing limitations or environmental occlusions. We model the interaction using a bank of zero-sum differential games, each corresponding to a distinct attacker-target hypothesis. To address informational asymmetry and partial observability, defenders adopt a belief-driven strategy selection mechanism inspired by the concept of Consistent Conjectural Nash Equilibrium (CCNE). In this framework, defenders form and iteratively refine a consistent hypothesis of the attacker’s intent by verifying trajectory consistency and propagating state estimates using precomputed feedback controllers during invisibility phases. This process ensures that strategy updates align with observed behavior, leading to accurate inference over time. We present formal problem formulations for both full and intermittent visibility regimes, and validate the framework through simulations demonstrating robust interception and coordinated multi-agent defense under uncertainty.

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Consistent Conjectural Approach to Adversarial Intent Tracking Under Sensing Constraints in Multi-target Defense Differential Games

  • Sharad Kumar Singh,
  • Quanyan Zhu

摘要

This paper addresses a multi-agent target defense differential game in which multiple static targets are protected by visibility-constrained autonomous defenders against a rational attacker. The attacker privately selects a fixed intended target from the set of static targets, while defenders experience intermittent access to the attacker’s state due to periodic sensing limitations or environmental occlusions. We model the interaction using a bank of zero-sum differential games, each corresponding to a distinct attacker-target hypothesis. To address informational asymmetry and partial observability, defenders adopt a belief-driven strategy selection mechanism inspired by the concept of Consistent Conjectural Nash Equilibrium (CCNE). In this framework, defenders form and iteratively refine a consistent hypothesis of the attacker’s intent by verifying trajectory consistency and propagating state estimates using precomputed feedback controllers during invisibility phases. This process ensures that strategy updates align with observed behavior, leading to accurate inference over time. We present formal problem formulations for both full and intermittent visibility regimes, and validate the framework through simulations demonstrating robust interception and coordinated multi-agent defense under uncertainty.