Advertisement

Clustering and Planning for Rescue Agent Simulation

  • Ahmed Abouraya
  • Dina Helal
  • Fadwa Sakr
  • Noha Khater
  • Salma Osama
  • Slim Abdennadher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

The paper describes the contribution of the GUC_ArtSapience team to the Rescue Agent Simulation competition in RoboCup in terms of the current research approach. The approach is divided into two parts: clustering and planning. Clustering is done through task allocation to divide the map among the agents. Planning is done after assigning the agents to parts of the map to determine how they should cooperate and coordinate together and how they should prioritize their tasks [2]. The agents can coordinate together using centers and communication if available or dynamically without the use of communication.

Keywords

Communication Channel Task Allocation Police Agent Message Size Fire Brigade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Guc artsapience team description paper (2012)Google Scholar
  2. 2.
    Multi-agent planning for the robocup rescue simulation: Applying clustering into task allocation and coordination. In: ICAART 2012 International Conference on Agents and Artificial Intelligence (2012)Google Scholar
  3. 3.
  4. 4.
    Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms, vol. 8(2006-13), pp. 1027–1035 (2007)Google Scholar
  5. 5.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 881–892 (2002)CrossRefGoogle Scholar
  6. 6.
    Winkler, R., Klawonn, F., Kruse, R.: Problems of fuzzy c-means clustering and similar algorithms with high dimensional data sets. In: Gaul, W.A., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds.) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 79–87. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ahmed Abouraya
    • 1
  • Dina Helal
    • 1
  • Fadwa Sakr
    • 1
  • Noha Khater
    • 1
  • Salma Osama
    • 1
  • Slim Abdennadher
    • 1
  1. 1.German University in CairoCairoEgypt

Personalised recommendations