{"type":"ScholarlyArticle","title":"On the Power and Limitations of Deception in Multi-Robot Adversarial Patrolling","authors":[{"name":"Noga Talmor","slug":"noga-talmor","given":"Noga","family":"Talmor","affiliation":"Department of Computer Science , Bar-Ilan University , Israel"},{"name":"Noa Agmon","slug":"noa-agmon","given":"Noa","family":"Agmon","affiliation":"Department of Computer Science , Bar-Ilan University , Israel"}],"abstract":"Multi-robot adversarial patrolling is a well studied problem, investigating how defenders can optimally use all given resources for maximizing the probability of detecting penetrations, that are controlled by an adversary. It is commonly assumed that the adversary in this problem is rational, thus uses the knowledge it has on the patrolling robots (namely, the number of robots, their location, characteristics and strategy) to optimize its own chances to penetrate successfully. In this paper we present a novel defending approach which manipulates the adversarial (possibly partial) knowledge on the patrolling robots, so that it will believe the robots have more power than they actually have. We describe two different ways of deceiving the adversary: Window Deception, in which it is assumed that the adversary has partial observability of the perimeter, and Scarecrow Deception, in which some of the patrolling robots only appear as real robots, though they have no ability to actually detect the adversary. We analyze the limitations of both models, and suggest a random-based approach for optimally deceiving the adversary that considers both the resources of the defenders, and the adversarial knowledge.","doi":"10.24963/ijcai.2017/61","url":"https://ijcai.org/proceedings/2017/61","pdf":"https://ijcai.org/proceedings/2017/0061.pdf","markdown":"https://ijcai.org/proceedings/2017/61.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/61","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":"Agent-based and Multi-agent Systems","track":"Main track","number":61,"pages":{"first":430,"last":436},"keywords":["Robotics and Vision: Motion and Path Planning","Robotics and Vision: Multi-Robot Systems","Agent-based and Multi-agent Systems: Noncooperative Games"],"videos":[],"references":[{"position":1,"raw":"[Agmon et al., 2008a] Noa Agmon, Sarit Kraus, and Gal A Kaminka. 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