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Information Fusion in Multi-agent System Based on Reliability Criterion

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Vision Based Systemsfor UAV Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 481))

Abstract

The paper addresses the problem of information fusion in Multi-Agent System. Since the knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to asses its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. The effectiveness of the method proposed is evaluated using relevant simulation experiments.

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References

  1. Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logiccontroller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)

    Google Scholar 

  3. Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 1969–1974 (2003)

    Google Scholar 

  4. Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage Convention District, Anchorage, Alaska, USA (2002)

    Google Scholar 

  5. Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)

    Article  Google Scholar 

  6. Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)

    Article  Google Scholar 

  7. Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  8. Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)

    Article  MATH  Google Scholar 

  9. Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots 14, 255–263 (2003)

    Article  MATH  Google Scholar 

  10. Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactionson Robotics and Automation 14(5), 815–822 (1998)

    Article  Google Scholar 

  11. Winkfeld, K.J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence (75), 297–345 (1995)

    Google Scholar 

  12. Wooldridge, M.: An Introduction to Multiagent Systems. Johnn Wiley and Sons Ltd., UK (2009) ISBN:978-0-470-51946-2

    Google Scholar 

  13. Vail, D., Veloso, M.: Dynamic Multi-Robot Coordination. In: Schultz, A., et al. (eds.) Multi Robot Systems: From Swarms to Intelligent Automata, vol. II, pp. 87–98. Kluwer Academic Publishers, The Netherlands (2003)

    Google Scholar 

  14. Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Gałuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K.: Planning as Artifficial Intelligence Problem-short introduction and overview. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 95–104. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Jędrasiak, K., Bereska, D., Nawrat, A.: The Prototype of Gyro-Stabilized UAV Gimbal for Day-Night Surveillance. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 107–116. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  17. Galuszka, A., Bereska, D., Simek, K., Skrzypczyk, K., Daniec, K.: Application of graphs theory methods to criminal analysis system. Przeglad Elektrotechniczny 86(9), 278–283 (2010)

    Google Scholar 

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Correspondence to Martin Mellado .

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Mellado, M., Skrzypczyk, K. (2013). Information Fusion in Multi-agent System Based on Reliability Criterion. In: Nawrat, A., Kuś, Z. (eds) Vision Based Systemsfor UAV Applications. Studies in Computational Intelligence, vol 481. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00369-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-00369-6_13

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00368-9

  • Online ISBN: 978-3-319-00369-6

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