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A Dynamical Characterization of Case-Based Reasoning Systems for Improving Its Performance in Highly Dynamic Environments

  • Luis F. Castillo
  • M. G. Bedia
  • M. Aguilera
  • L. Uribe
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 91)

Abstract

In this paper a mathematical technique is presented for modelling the generation of solutions in a standard-CBR methodology adapted to highly dynamic environments. In recent years, much research has focused on exploring how to improve CBR-systems to deal with dynamic environments where changes are difficult to model or predict and, consequently, the performance of the CBRs gets worse with time. High-level planning, reactive strategies or hybrid alternatives have been proposed to face this problem, but this contribution will not be related on particular techniques.We simply concentrate on formal aspects without establishing which should be the most adequate procedure in a subsequent implementation stage. The advantage of the presented scheme is that it does not depend on neither the problem nor the model of the environment. It consists in a formal approach that only requires, local information about the averaged-time spent by the system in obtaining a solution and an estimated measure about the dynamism of the environment.

Keywords

Dynamic Environment Case Base Reasoning Formal Aspect Greedy Strategy Dynamical Characterization 
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.

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References

  1. [Aamodt et al., 1994]
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  2. [Bellman, 1957]
    Bellman, R.E.: Dynamic Programming. Princeton University Press, Princeton (1957) (republished 2003: Dover), ISBN 0486428095zbMATHGoogle Scholar
  3. [Berenji et al., 2005]
    Berenji, H., Wang, Y., Saxena, A.: Dynamic Case Based Reasoning in Fault Diagnostics and Prognostics. In: FUZZ-IEEE, Reno (May 2005)Google Scholar
  4. [Mehta et al., 2010]
    Mehta, M., Ram, A.: Run-Time Behavior Adaptation for Real-Time Interactive Games. IEEE Transactions on Computational Intelligence and AI in Games 1(3) (September 2009)Google Scholar
  5. [Mehta et al., 2010 (b)]
    Mehta, M., Ontañón, S., Ram, A.: Using Meta-reasoning to Improve the Performance of Case-Based Planning. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS, vol. 5650, pp. 210–224. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. [Ontanon et al., 2010]
    Ontanon, S., Mishra, K., Sugandh, N., Ram, A.: On-Line Case-Based Planning. In: Computational Intelligence, pp. 84–119 (2010)Google Scholar
  7. [Urdiales et al., 2003]
    Urdiales, C., Perez, J., Vázquez-Salceda, J., Sandoval, F.: A hybrid architecture for autonomous navigation using a CBR reactive layer. In: Proceedings of the 2003 IEEE/WIC International Conference on Intelligent Agent Technology (IAT 2003), Halifax, Canada, October 13-16, pp. 225–232. IEEE Computer Society, Los Alamitos (2003), ISBN 0-7695-1931-8Google Scholar
  8. [Watson, 1999]
    Watson, I.: CBR is a methodology not a technology. Knowledge Based Systems Journal 12(5-6), 303–308 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luis F. Castillo
    • 1
  • M. G. Bedia
    • 2
  • M. Aguilera
    • 2
  • L. Uribe
    • 3
  1. 1.Industrial engineeringNational University of ColombiaColombia
  2. 2.Computer ScienceUniversity of ZaragozaSpain
  3. 3.Computer ScienceAutonomous University of ManizalesColombia

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