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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 have 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, we apply enumerative combinatorics to characterise the penetration detection probabilities. 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, our methods significantly reduces the time and space complexity of solving the problem. We use this method to tackle four penetration configurations.
Robot Planning, Planning under Uncertainty, Theoretical Foundations of Planning
Buermann, Jan
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Zhang, Jie
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Buermann, Jan
d12aad54-d71f-4eba-9510-daf0ee2c7fc3
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a

Buermann, Jan and Zhang, Jie (2020) Multi-robot adversarial patrolling strategies via lattice paths. 29th International Joint Conference on Artificial Intelligence, Japan. 11 - 17 Jul 2020. 7 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In full-knowledge multi-robot adversarial patrolling, a group of robots have 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, we apply enumerative combinatorics to characterise the penetration detection probabilities. 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, our methods significantly reduces the time and space complexity of solving the problem. We use this method to tackle four penetration configurations.

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Multi-Robot_Adversarial_Patrolling_Strategies_via_Lattice_Paths - Accepted Manuscript
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More information

Accepted/In Press date: 20 April 2020
Published date: July 2020
Venue - Dates: 29th International Joint Conference on Artificial Intelligence, Japan, 2020-07-11 - 2020-07-17
Keywords: Robot Planning, Planning under Uncertainty, Theoretical Foundations of Planning

Identifiers

Local EPrints ID: 440630
URI: http://eprints.soton.ac.uk/id/eprint/440630
PURE UUID: 99fffbba-e4ca-4fba-8c91-fc544b6a9113
ORCID for Jan Buermann: ORCID iD orcid.org/0000-0002-4981-6137
ORCID for Jie Zhang: ORCID iD orcid.org/0000-0002-5348-7671

Catalogue record

Date deposited: 12 May 2020 16:46
Last modified: 23 May 2020 00:40

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