research-article

Speeding up moving-target search

Authors:

Sven Koenig,

Maxim Likhachev,

Xiaoxun SunAuthors Info & Claims

AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems

Article No.: 188, Pages 1 - 8

https://doi.org/10.1145/1329125.1329353

Published: 14 May 2007 Publication History

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Abstract

In this paper, we study moving-target search, where an agent (= hunter) has to catch a moving target (= prey). The agent does not necessarily know the terrain initially but can observe it within a certain sensor range around itself. It uses the strategy to always move on a shortest presumed unblocked path toward the target, which is a reasonable strategy for computer-controlled characters in video games. We study how the agent can find such paths faster by exploiting the fact that it performs A* searches repeatedly. To this end, we extend Adaptive A*, an incremental heuristic search method, to moving-target search and demonstrate experimentally that the resulting MT-Adaptive A* is faster than isolated A* searches and, in many situations, also D* Lite, a state-of-the-art incremental heuristic search method. In particular, it is faster than D* Lite by about one order of magnitude for moving-target search in known and initially unknown mazes if both search methods use the same informed heuristics.

References

[1]

M. Goldenberg, A. Kovarksy, X. Wu, and J. Schaeffer. Multiple agents moving target search. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1538--1538, 2003.

Digital Library

Google Scholar

[2]

P. Hart, N. Nilsson, and B. Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 2:100--107, 1968.

Crossref

Google Scholar

[3]

R. Holte, T. Mkadmi, R. Zimmer, and A. MacDonald. Speeding up problem solving by abstraction: A graph oriented approach. Artificial Intelligence, 85(1--2):321--361, 1996.

Digital Library

Google Scholar

[4]

T. Ishida and R. Korf. Moving target search. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 204--210, 1991.

Digital Library

Google Scholar

[5]

S. Koenig and M. Likhachev. D* Lite. In Proceedings of the National Conference on Artificial Intelligence, pages 476--483, 2002.

Digital Library

Google Scholar

[6]

S. Koenig and M. Likhachev. Incremental A*. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 1539--1546, Cambridge, MA, 2002. MIT Press.

Google Scholar

[7]

S. Koenig and M. Likhachev. Adaptive A*. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1311--1312, 2005.

Digital Library

Google Scholar

[8]

S. Koenig and M. Likhachev. A new principle for incremental heuristic search: Theoretical results. In Proceedings of the International Conference on Automated Planning and Scheduling, pages 410--413, 2006.

Google Scholar

[9]

S. Koenig and M. Likhachev. Real-Time Adaptive A*. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, pages 281--288, 2006.

Digital Library

Google Scholar

[10]

S. Koenig, M. Likhachev, and D. Furcy. Lifelong Planning A*. Artificial Intelligence, 155(1--2):93--146, 2004.

Digital Library

Google Scholar

[11]

S. Koenig and R. Simmons. Real-time search in non-deterministic domains. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1660--1667, 1995.

Digital Library

Google Scholar

[12]

M. Likhachev and S. Koenig. Speeding up the Parti-Game algorithm. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 1563--1570, Cambridge, MA, 2002. MIT Press.

Google Scholar

[13]

J. Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, 1985.

Digital Library

Google Scholar

[14]

A. Stentz. The focussed D* algorithm for real-time replanning. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1652--1659, 1995.

Digital Library

Google Scholar

Cited By

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Nussbaum DIlitchev F(2023)Chasing an intruder with limited informationInternational Journal of Intelligent Robotics and Applications10.1007/s41315-023-00286-y7:4(652-670)Online publication date: 22-Aug-2023
https://doi.org/10.1007/s41315-023-00286-y
Afzalov ALotfi AInden BAydin M(2022)A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple AgentsSN Computer Science10.1007/s42979-022-01302-x3:6Online publication date: 9-Aug-2022
https://doi.org/10.1007/s42979-022-01302-x
Nussbaum D(2021)Energy Conserving Path when Chasing an Intruder by Autonomous Guards2021 IEEE International Conference on Mechatronics and Automation (ICMA)10.1109/ICMA52036.2021.9512628(152-157)Online publication date: 8-Aug-2021
https://doi.org/10.1109/ICMA52036.2021.9512628
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Index Terms

Speeding up moving-target search
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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Published In

AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems

May 2007

1585 pages

ISBN:9788190426275

DOI:10.1145/1329125

Conference Chairs:
Edmund Durfee
University of Michigan
,
Makoto Yokoo
Kyushu University
,
Program Chairs:
Michael Huhns
University of South Carolina
,
Onn Shehory
IBM Haifa Research Lab, Israel

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2007

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Division of Information and Intelligent Systems

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AAMAS07

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AAMAS07: International Conference on Autonomous Agents and Mulitagent Systems

May 14 - 18, 2007

Hawaii, Honolulu

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

View all

Nussbaum DIlitchev F(2023)Chasing an intruder with limited informationInternational Journal of Intelligent Robotics and Applications10.1007/s41315-023-00286-y7:4(652-670)Online publication date: 22-Aug-2023
https://doi.org/10.1007/s41315-023-00286-y
Afzalov ALotfi AInden BAydin M(2022)A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple AgentsSN Computer Science10.1007/s42979-022-01302-x3:6Online publication date: 9-Aug-2022
https://doi.org/10.1007/s42979-022-01302-x
Nussbaum D(2021)Energy Conserving Path when Chasing an Intruder by Autonomous Guards2021 IEEE International Conference on Mechatronics and Automation (ICMA)10.1109/ICMA52036.2021.9512628(152-157)Online publication date: 8-Aug-2021
https://doi.org/10.1109/ICMA52036.2021.9512628
Stern RGoldenberg MSaffidine AFelner A(2021)Heuristic search for one-to-many shortest path queriesAnnals of Mathematics and Artificial Intelligence10.1007/s10472-021-09775-xOnline publication date: 17-Nov-2021
https://doi.org/10.1007/s10472-021-09775-x
Afzalov ALotfi AAydin M(2021)A Strategic Search Algorithm in Multi-agent and Multiple Target EnvironmentRiTA 202010.1007/978-981-16-4803-8_21(195-204)Online publication date: 5-Aug-2021
https://doi.org/10.1007/978-981-16-4803-8_21
Afzalov AHe JLotfi AAydin M(2021)Multi-Agent Path Planning Approach Using Assignment Strategy Variations in Pursuit of Moving TargetsAgents and Multi-Agent Systems: Technologies and Applications 202110.1007/978-981-16-2994-5_38(451-463)Online publication date: 8-Jun-2021
https://doi.org/10.1007/978-981-16-2994-5_38
Nussbaum DIlitchev F(2020)Catching a Robot Intruder with Limited Information2020 Fourth IEEE International Conference on Robotic Computing (IRC)10.1109/IRC.2020.00010(17-23)Online publication date: Nov-2020
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Zhu QWang KShao ZBiegler L(2020)Receding Horizon Optimization Method for Solving the Cops and Robbers Problems in a Complex Environment with ObstaclesJournal of Intelligent & Robotic Systems10.1007/s10846-020-01188-yOnline publication date: 17-Jul-2020
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Mittal AJain AKumar ATiwari R(2018)Pursuit-Evasion: Multiple Pursuer Pursue Multiple Evader Using WaveFront and Hungarian MethodProceedings of the International Conference on Computing and Communication Systems10.1007/978-981-10-6890-4_46(473-488)Online publication date: 30-Mar-2018
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Xie FBotea AKishimoto A(2017)A scalable approach to chasing multiple moving targets with multiple agentsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171911(4470-4476)Online publication date: 19-Aug-2017
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