Goal Recognition Control under Network Interdiction Using a Privacy Information Metric

Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor's behavior and accelerate goal recognition by reducing the uncertainty, the actor wants to minimize the privacy information leakage by manipulating the asymmetric information and delay the goal recognition process. Then, the GRC under network interdiction is formulated as one static Stackelberg game, where the observer obtains asymmetric information about the actor's intended goal and proactively interdicts the edges of the network with a bounded resource. The privacy leakage of the actor's actions about the real goals is quantified by a min-entropy information metric and this privacy information metric is associated with the goal uncertainty. Next in importance, we define the privacy information metric based GRC under network interdiction (InfoGRC) and the information metric based GRC under threshold network interdiction (InfoGRCT). After dual reformulating, the InfoGRC and InfoGRCT as bi-level mixed-integer programming problems, one Benders decomposition-based approach is adopted to optimize the observer's optimal interdiction resource allocation and the actor's cost-optimal path-planning. Finally, some experimental evaluations are conducted to demonstrate the effectiveness of the InfoGRC and InfoGRCT models in the task of controlling the goal recognition process.