Speed vs Accuracy in Goal Recognition for Time-Sensitive Applications: a Game-Theoretic Approach

This work addresses a specific instance of Goal Recognition (GR), termed time-sensitive GR, where a malicious actor (the attacker) seeks to reach and damage one of several sensitive targets, while the observer (the defender) must identify the attacker's target and allocate limited resources to protect it. Focusing on real-world physical and cyber security scenarios, the defender faces a tradeoff between acting early, with limited information, or waiting for more data but risking insufficient time to defend. Our contributions include introducing a game-theoretic formulation of this instance of GR, which captures the time-sensitive nature of these scenarios, and providing an efficient method to compute Nash equilibria using the fictitious play learning scheme. Experimental results confirm that our method equips the defender with robust policies, outperforming less adaptable strategies.