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.
Original language | English |
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Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Editors | Carles Sierra |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 430-436 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
State | Published - 2017 |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 0 |
ISSN (Print) | 1045-0823 |
Conference
Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |
Bibliographical note
Funding Information:∗This research was funded in part by ISF grant 1337/15.
Funding
∗This research was funded in part by ISF grant 1337/15.
Funders | Funder number |
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Israel Science Foundation | 1337/15 |