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Tunably decentralized algorithms for cooperative target observation

Published: 25 July 2005 Publication History

Abstract

Multi-agent problem domains may require distributed algorithms for a variety of reasons: local sensors, limitations of communication, and availability of distributed computational resources. In the absence of these constraints, centralized algorithms are often more efficient, simply because they are able to take advantage of more information. We introduce a variant of the cooperative target observation domain which is free of such constraints. We propose two algorithms, inspired by K-means clustering and hill-climbing respectively, which are scalable in degree of decentralization. Neither algorithm consistently outperforms the other across over all problem domain settings. Surprisingly, we find that hill-climbing is sensitive to degree of decentralization, while K-means is not. We also experiment with a combination of the two algorithms which draws strength from each.

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  • (2023)Smart Organizations of Unmanned Aerial Vehicles Using SOMs for Monitoring Cars on Urban Roads2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 Workshop on Robotics in Education (WRE)10.1109/LARS/SBR/WRE59448.2023.10332968(83-88)Online publication date: 9-Oct-2023
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cover image ACM Conferences
AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
July 2005
1407 pages
ISBN:1595930930
DOI:10.1145/1082473
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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Publication History

Published: 25 July 2005

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Author Tags

  1. K-means clustering
  2. hill-climbing
  3. multiagent systems

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

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Cited By

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  • (2024)Simultaneous Search and Tracking of Non-cooperative Mobile Targets Using Multiple UAVs in Uneven EnvironmentsInternational Journal of Aeronautical and Space Sciences10.1007/s42405-024-00750-4Online publication date: 24-May-2024
  • (2024)Multi‐objective Multi‐agent Decision‐MakingSystems Science for Engineers and Scholars10.1002/9781394211678.ch17(391-409)Online publication date: Mar-2024
  • (2023)Smart Organizations of Unmanned Aerial Vehicles Using SOMs for Monitoring Cars on Urban Roads2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 Workshop on Robotics in Education (WRE)10.1109/LARS/SBR/WRE59448.2023.10332968(83-88)Online publication date: 9-Oct-2023
  • (2021)Comparison Between A* and RRT Algorithms for 3D UAV Path PlanningUnmanned Systems10.1142/S230138502250007810:02(129-146)Online publication date: 8-Oct-2021
  • (2021)Cooperative Observation of Malicious Targets in a 3D Urban Traffic Environment Using UAVs2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE)10.1109/LARS/SBR/WRE54079.2021.9605390(60-65)Online publication date: 11-Oct-2021
  • (2021)Intelligent Agents for Observation and Containment of Malicious Targets OrganizationsIntelligent Systems10.1007/978-3-030-91702-9_4(48-63)Online publication date: 28-Nov-2021
  • (2021)Cooperative Monitoring of Malicious Activity in Stock ExchangesTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-030-75015-2_13(121-132)Online publication date: 3-May-2021
  • (2020)Improving the Behavior of Evasive Targets in Cooperative Target Observation2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC48688.2020.00015(36-41)Online publication date: Jul-2020
  • (2020)Cooperative Target Observation using Density-based Clustering with Self-tuning and a New Grid Environment2020 XLVI Latin American Computing Conference (CLEI)10.1109/CLEI52000.2020.00011(33-38)Online publication date: Oct-2020
  • (2020)Cooperative Observation of Smart Target AgentsIntelligent Systems10.1007/978-3-030-61380-8_6(77-92)Online publication date: 13-Oct-2020
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