Multiagent Patrol Generalized to Complex Environmental Conditions

Noa Agmon, Daniel Urieli, Peter Stone

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

3 Scopus citations

Abstract

The problem of multiagent patrol has gained considerable attention during the past decade, with the immediate applicability of the problem being one of its main sources of interest. In this paper we concentrate on frequency-based patrol, in which the agents' goal is to optimize a frequency criterion, namely, minimizing the time between visits to a set of interest points. We consider multiagent patrol in environments with complex environmental conditions that affect the cost of traveling from one point to another. For example, in marine environments, the travel time of ships depends on parameters such as wind, water currents, and waves. We demonstrate that in such environments there is a need to consider a new multiagent patrol strategy which divides the given area into parts in which more than one agent is active, for improving frequency. We show that in general graphs this problem is intractable, therefore we focus on simplified (yet realistic) cyclic graphs with possible inner edges. Although the problem remains generally intractable in such graphs, we provide a heuristic algorithm that is shown to significantly improve point-visit frequency compared to other patrol strategies. For evaluation of our work we used a custom developed ship simulator that realistically models ship movement constraints such as engine force and drag and reaction of the ship to environmental changes.

Original languageEnglish
Title of host publicationProceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011
PublisherAAAI press
Pages1090-1095
Number of pages6
ISBN (Electronic)9781577355083
StatePublished - 11 Aug 2011
Externally publishedYes
Event25th AAAI Conference on Artificial Intelligence, AAAI 2011 - San Francisco, United States
Duration: 7 Aug 201111 Aug 2011

Publication series

NameProceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

Conference

Conference25th AAAI Conference on Artificial Intelligence, AAAI 2011
Country/TerritoryUnited States
CitySan Francisco
Period7/08/1111/08/11

Bibliographical note

Publisher Copyright:
Copyright © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

∗This work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (IIS-0917122), ONR (N00014-09-1-0658), and the FHWA (DTFH61-07-H-00030). Copyright ©c 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 1Of course higher dimensions are also possible.

FundersFunder number
National Science FoundationIIS-0917122
Office of Naval ResearchN00014-09-1-0658
Federal Highway AdministrationDTFH61-07-H-00030

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