Sean Luke
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.
- T. Balch. Behavioral Diversity in Learning Robot Teams. PhD thesis, College of Computing, Georgia Institute of Technology, 1998. Google Scholar
Digital Library
- Y. Bar-Shalom, editor. Multitarget-multisensor Tracking. Artech House, 1990.Google Scholar
- R. Beckers, O. E. Holland, and J.-L. Deneubourg. From local actions to global tasks: Stigmergy and collective robotics. In Artificial Life IV: Proceedings of the International Workshop on the Synthesis and Simulation of Living Systems, third edition. MIT Press, 1994.Google Scholar
- A. Billard, A. Ijspeert, and A. Martinoli. A multi-robot system for adaptive exploration of a fast changing environment: Probabilistic modeling and experimental study. Connection Science, 11:359--379, 1999.Google ScholarCross Ref
- S. S. Blackman. Multiple-Target Tracking with Radar Applications. Artech House, 1986.Google Scholar
- T. S. Dahl, M. J. Mataric, and G. S. Sukhatme. Adaptive spatio-temporal organization in groups of robots. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-02), 2002.Google ScholarCross Ref
- J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, and L. Chretien. The dynamics of collective sorting: Robot-like ants and ant-like robots. In From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pages 356--363. MIT Press, 1991. Google ScholarDigital Library
- D. Fox, W. Burgard, H. Kruppa, and S. Thrun. A probabilistic approach to collaborative multi-robot localization. Autonomous Robots, 8(3):325--344, 2000. Google ScholarDigital Library
- A. J. Ijspeert, A. Martinoli, A. Billard, and L. M. Gambardella. Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots, 11(2), 2001. Google ScholarDigital Library
- S. LaValle, H. Gonzalez-Banos, C. Becker, and J. Latombe. Motion strategies for maintaining visibility of a moving target. In Proceedings of IEEE International Conference on Robotics and Automation, 1997.Google ScholarCross Ref
- L. Li, A. Martinoli, and Y. S. Abu-Mostafa. Diversity and specialization in collaborative swarm systems. In T. Balch and C. Anderson, editors, Proceedings of the 2nd International Workshop on the Mathematics and Algorithms of Social Insects, pages 91--98, Atlanta, Georgia, USA, Dec. 15--17 2003.Google Scholar
- S. Luke, G. C. Balan, L. A. Panait, C. Cioffi-Revilla, and S. Paus. MASON: a Java multi-agent simulation library. In Proceedings of Agent 2003 Conference on Challenges in Social Simulation, 2003.Google Scholar
- A. Martinoli and F. Mondada. Collective and cooperative group behaviours: Biologically inspired experiments in robotics. In Proceedings of the Fourth Symposium on Experimental Robotics, ISER-95, 1995. Google ScholarDigital Library
- M. J. Mataric. Interaction and Intelligent Behavior. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 1994. Also Technical Report AITR-1495. Google ScholarDigital Library
- L. Parker. Alliance: An architecture for fault tolerance multi-robot cooperation. AIEEE Transactions on Robotics and Automation, 14(2), 1998.Google Scholar
- L. Parker. Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous Robots, 12(3):231--255, 2002. Google ScholarDigital Library
- L. Parker and B. Emmons. Cooperative multi-robot observation of multiple moving targets. In Proceedings of 1997 International Conference on Robotics and Automation, pages 2082--2089, 1997.Google ScholarCross Ref
- L. Parker and C. Touzet. Multi-robot learning in a cooperative observation task. In J. B. Lynne Pakers, George Bekey, editor, Robotic Systems 4, pages 391--401. Springer, 2000.Google Scholar
- C. Touzet. Robot awareness in cooperative mobile robot learning. Autonomous Robots, 2:1--13, 2000. Google ScholarDigital Library
- B. B. Werger and M. Mataric. Broadcast of local eligibility for multi-target observation. In Proceedings, 5th International Symposium on Distributed Autonomous Robotic Systems (DARS), pages 347--356, 2000.Google ScholarCross Ref
Index Terms
- Tunably decentralized algorithms for cooperative target observation
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