Online probabilistic goal recognition and its application in dynamic shortest-path local network interdiction.

Goal recognition is the task of inferring an agent’s goals given some or all of the agent’s observed actions. However, few research focuses on how to improve the usage effectiveness of knowledge produced by a goal recognition system. In this work, we propose a probabilistic goal recognition approach tailored to a dynamic shortest-path network interdiction problem. Apart from inferring a probabilistic distribution over the possible goals of an agent, our work has another four key novelties: (i) a dynamic shortest-path local network interdiction model that allocates resources locally per step using goal recognition information; (ii) two behavior modeling approaches, including a data-driven learning method based on Inverse Reinforcement Learning as well as a heuristic method taking advantage of the network information, to help solve both the data-intensive and no available data situations; (iii) a heuristic named Subjective Confidence that uses variance in particle system for flexible resource allocation adjustment. The empirical test results show the effectiveness of our goal recognition method, and also verify the practical implications of these methods in solving scalable multi-terminus network interdiction problem.