On the power and limitations of Deception in multi-robot adversarial patrolling

Noga Talmor, Noa Agmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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 languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages430-436
Number of pages7
ISBN (Electronic)9780999241103
DOIs
StatePublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN (Print)1045-0823

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/TerritoryAustralia
CityMelbourne
Period19/08/1725/08/17

Bibliographical note

Funding Information:
∗This research was funded in part by ISF grant 1337/15.

Fingerprint

Dive into the research topics of 'On the power and limitations of Deception in multi-robot adversarial patrolling'. Together they form a unique fingerprint.

Cite this