{"type":"ScholarlyArticle","title":"Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction","authors":[{"name":"Kai Xu","slug":"kai-xu","given":"Kai","family":"Xu","affiliation":". The Institute of Simulation Engineering ,"},{"name":"Kaiming Xiao","slug":"kaiming-xiao","given":"Kaiming","family":"Xiao","affiliation":". Science and Technology on Information Systems Engineering Laboratory , College of Information System and Management , NUDT , Changsha , 410073 , China."},{"name":"Quanjun Yin","slug":"quanjun-yin","given":"Quanjun","family":"Yin","affiliation":". The Institute of Simulation Engineering ,; College of Information System and Management , NUDT , Changsha , 410073 , China."},{"name":"Yabing Zha","slug":"yabing-zha","given":"Yabing","family":"Zha","affiliation":". The Institute of Simulation Engineering ,"},{"name":"Cheng Zhu","slug":"cheng-zhu","given":"Cheng","family":"Zhu","affiliation":". Science and Technology on Information Systems Engineering Laboratory , College of Information System and Management , NUDT , Changsha , 410073 , China."}],"abstract":"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 researches focus 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.","doi":"10.24963/ijcai.2017/625","url":"https://ijcai.org/proceedings/2017/625","pdf":"https://ijcai.org/proceedings/2017/0625.pdf","markdown":"https://ijcai.org/proceedings/2017/625.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/625","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Planning and Scheduling","track":"Main track","number":625,"pages":{"first":4477,"last":4483},"keywords":["Planning and Scheduling: Activity and Plan Recognition","Uncertainty in AI: Approximate Probabilistic Inference","Uncertainty in AI: Sequential Decision Making","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Ahlberg et al., 2007] Simon Ahlberg, Pontus Hörling, Kata- rina Johansson, Karsten Jöred, Hedvig Kjellström, Chris- tian Mårtenson, Göran Neider, Johan Schubert, Pon- tus Svenson, Per Svensson, et al. 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