An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments

James C Ward; Edmund R Hunt

doi:10.1145/3555776.3577802

An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments

James C Ward, Edmund R Hunt

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (Scopus)

168 Downloads (Pure)

Abstract

In the field of multi-robot patrolling, graph-based environment models are a popular approach for designing and testing distributed multi-robot patrol strategies. These strategies are typically optimized for regular visiting of the vertices of the patrol graphs. However, analysis of these strategies against potential attackers is limited. We present an empirical, simulation-based method to assess performance of multi-agent patrol strategies against potential adversaries by estimating the probability of simulated attackers succeeding against the patrol agents. We show that this approach can provide new insights into performance that would not be found in standard non-adversarial analysis.

Original language	English
Title of host publication	Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023
Subtitle of host publication	Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS)
Publisher	Association for Computing Machinery (ACM)
Pages	787-790
Number of pages	4
ISBN (Electronic)	9781450395175
DOIs	https://doi.org/10.1145/3555776.3577802
Publication status	Published - 7 Jul 2023

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

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© 2023 Owner/Author(s).

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Ward, J. C., & Hunt, E. R. (2023). An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023: Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS) (pp. 787-790). (Proceedings of the ACM Symposium on Applied Computing). Association for Computing Machinery (ACM). https://doi.org/10.1145/3555776.3577802

Ward, James C ; Hunt, Edmund R. / An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023: Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS). Association for Computing Machinery (ACM), 2023. pp. 787-790 (Proceedings of the ACM Symposium on Applied Computing).

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title = "An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments",

abstract = "In the field of multi-robot patrolling, graph-based environment models are a popular approach for designing and testing distributed multi-robot patrol strategies. These strategies are typically optimized for regular visiting of the vertices of the patrol graphs. However, analysis of these strategies against potential attackers is limited. We present an empirical, simulation-based method to assess performance of multi-agent patrol strategies against potential adversaries by estimating the probability of simulated attackers succeeding against the patrol agents. We show that this approach can provide new insights into performance that would not be found in standard non-adversarial analysis.",

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language = "English",

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Ward, JC & Hunt, ER 2023, An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments. in Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023: Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS). Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery (ACM), pp. 787-790. https://doi.org/10.1145/3555776.3577802

An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments. / Ward, James C ; Hunt, Edmund R.
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023: Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS). Association for Computing Machinery (ACM), 2023. p. 787-790 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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T1 - An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments

AU - Ward, James C

AU - Hunt, Edmund R

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N2 - In the field of multi-robot patrolling, graph-based environment models are a popular approach for designing and testing distributed multi-robot patrol strategies. These strategies are typically optimized for regular visiting of the vertices of the patrol graphs. However, analysis of these strategies against potential attackers is limited. We present an empirical, simulation-based method to assess performance of multi-agent patrol strategies against potential adversaries by estimating the probability of simulated attackers succeeding against the patrol agents. We show that this approach can provide new insights into performance that would not be found in standard non-adversarial analysis.

AB - In the field of multi-robot patrolling, graph-based environment models are a popular approach for designing and testing distributed multi-robot patrol strategies. These strategies are typically optimized for regular visiting of the vertices of the patrol graphs. However, analysis of these strategies against potential attackers is limited. We present an empirical, simulation-based method to assess performance of multi-agent patrol strategies against potential adversaries by estimating the probability of simulated attackers succeeding against the patrol agents. We show that this approach can provide new insights into performance that would not be found in standard non-adversarial analysis.

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DO - 10.1145/3555776.3577802

M3 - Conference Contribution (Conference Proceeding)

T3 - Proceedings of the ACM Symposium on Applied Computing

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BT - Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023

PB - Association for Computing Machinery (ACM)

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Ward JC , Hunt ER. An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023: Technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS). Association for Computing Machinery (ACM). 2023. p. 787-790. (Proceedings of the ACM Symposium on Applied Computing). doi: 10.1145/3555776.3577802

An Empirical Method for Benchmarking Multi-Robot Patrol Strategies in Adversarial Environments

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