{"type":"ScholarlyArticle","title":"Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary","authors":[{"name":"Tien Mai","slug":"tien-mai","given":"Tien","family":"Mai","affiliation":"Singapore Management University"},{"name":"Avinandan Bose","slug":"avinandan-bose","given":"Avinandan","family":"Bose","affiliation":"University of Washington"},{"name":"Arunesh Sinha","slug":"arunesh-sinha","given":"Arunesh","family":"Sinha","affiliation":"Rutgers University"},{"name":"Thanh Nguyen","slug":"thanh-nguyen","given":"Thanh","family":"Nguyen","affiliation":"University of Oregon"},{"name":"Ayushman Kumar singh","slug":"ayushman-kumar-singh","given":"Ayushman","family":"Kumar singh","affiliation":"Indian Institute of Technology , Delhi"}],"abstract":"This work studies Stackelberg network interdiction games --- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly.  We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based (quantal response) discrete choice models. The resulting optimization is non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient  algorithms with  solution guarantees. First, we present a near optimal solution method based on path sampling, piece-wise linear approximation  and mixed-integer linear programming (MILP) reformulation. Second, we explore a dynamic programming based method, addressing the exponentially-many-path challenge. We then show that the gradient of the non-convex objective can also be computed in polynomial time, which allows us to use a gradient-based method to solve the problem efficiently.  Experiments based on instances of different sizes demonstrate the efficiency of our approach in achieving near-optimal solutions.","doi":"10.24963/ijcai.2024/323","url":"https://ijcai.org/proceedings/2024/323","pdf":"https://ijcai.org/proceedings/2024/0323.pdf","markdown":"https://ijcai.org/proceedings/2024/323.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/323","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Game Theory and Economic Paradigms","track":"Main Track","number":323,"pages":{"first":2913,"last":2921},"keywords":["Constraint Satisfaction and Optimization: CSO: Mixed discrete and continuous optimization","Game Theory and Economic Paradigms: GTEP: Noncooperative games"],"videos":[],"references":[{"position":1,"raw":"[Aguirregabiria and Mira, 2010] Victor Aguirregabiria and Pedro Mira. 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