Goal recognition, which is the task of inferring an agent's goals given some or all of the agent's observed actions, is one of the important approaches in bridging the gap between the observation and decision making within an observe-orient-decide-act cycle. Unfortunately, few research focuses on how to improve the utilization of knowledge produced by a goal recognition system. In this work, we propose a Markov Decision Process-based goal recognition approach tailored to a dynamic shortest-path local network interdiction (DSPLNI) problem. We first introduce a novel DSPLNI model and its solvable dual form so as to incorporate real-time knowledge acquired from goal recognition system. Then a Markov Decision Process-based goal recognition model along with its dynamic Bayesian network representation and the applied goal inference method is proposed to identify the evader's real goal within the DSPLNI context. Based on that, we further propose an efficient scalable technique in maintaining action utility map used in fast goal inference, and develop a flexible resource assignment mechanism in DSPLNI using knowledge from goal recognition system. Experimental results show the effectiveness and accuracy of our methods both in goal recognition and dynamic network interdiction.

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A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

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