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Multi-robot adversarial patrolling strategies via lattice paths

Multi-robot adversarial patrolling strategies via lattice paths
Multi-robot adversarial patrolling strategies via lattice paths
In full-knowledge multi-robot adversarial patrolling, a group of robots has to detect an adversary who knows the robots' strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities on polylines, we apply enumerative combinatorics to characterise the penetration detection probabilities for four penetration configurations. It allows us to provide the closed formulae of these probabilities and facilitates characterising optimal random defence strategies. Comparing to iteratively updating the Markov transition matrices, we empirically show that our method reduces the runtime by up to several hours. This allows us extensive simulations on the two dominant robot movement types for patrolling a perimeter showing that a movement with direction is up to 0.4 more likely to detect an adversary. Therefore, our approach greatly benefits the theoretical and empirical analysis of optimal patrolling strategies with extendability to more complicated attacks and other structured environments.
Multi-robot systems, Robots in adversarial settings
0004-3702
Buermann, Jan
46ae30cc-34e3-4a39-8b11-4cbb413e615f
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a
Buermann, Jan
46ae30cc-34e3-4a39-8b11-4cbb413e615f
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a

Buermann, Jan and Zhang, Jie (2022) Multi-robot adversarial patrolling strategies via lattice paths. Artificial Intelligence, 311, [103769]. (doi:10.1016/j.artint.2022.103769).

Record type: Article

Abstract

In full-knowledge multi-robot adversarial patrolling, a group of robots has to detect an adversary who knows the robots' strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities on polylines, we apply enumerative combinatorics to characterise the penetration detection probabilities for four penetration configurations. It allows us to provide the closed formulae of these probabilities and facilitates characterising optimal random defence strategies. Comparing to iteratively updating the Markov transition matrices, we empirically show that our method reduces the runtime by up to several hours. This allows us extensive simulations on the two dominant robot movement types for patrolling a perimeter showing that a movement with direction is up to 0.4 more likely to detect an adversary. Therefore, our approach greatly benefits the theoretical and empirical analysis of optimal patrolling strategies with extendability to more complicated attacks and other structured environments.

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Accepted/In Press date: 28 July 2022
e-pub ahead of print date: 2 August 2022
Published date: October 2022
Additional Information: Funding Information: This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) doctoral training grant EP/M508147/1.Jie Zhang was supported by a Leverhulme Trust Research Project Grant (2021 – 2024).
Keywords: Multi-robot systems, Robots in adversarial settings

Identifiers

Local EPrints ID: 469836
URI: http://eprints.soton.ac.uk/id/eprint/469836
ISSN: 0004-3702
PURE UUID: aaa96e68-2629-4420-b597-88efbddee16a
ORCID for Jan Buermann: ORCID iD orcid.org/0000-0002-4981-6137

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Date deposited: 27 Sep 2022 16:36
Last modified: 07 Jan 2023 03:04

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Contributors

Author: Jan Buermann ORCID iD
Author: Jie Zhang

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