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 language | English |
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Title of host publication | Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
Publisher | AAAI press |
Pages | 1090-1095 |
Number of pages | 6 |
ISBN (Electronic) | 9781577355083 |
State | Published - 11 Aug 2011 |
Externally published | Yes |
Event | 25th AAAI Conference on Artificial Intelligence, AAAI 2011 - San Francisco, United States Duration: 7 Aug 2011 → 11 Aug 2011 |
Publication series
Name | Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
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Conference
Conference | 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
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Country/Territory | United States |
City | San Francisco |
Period | 7/08/11 → 11/08/11 |
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.
Funders | Funder number |
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National Science Foundation | IIS-0917122 |
Office of Naval Research | N00014-09-1-0658 |
Federal Highway Administration | DTFH61-07-H-00030 |