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A two-level approach to learning in nonstationary environments

  • Wai Lam
  • Snehasis Mukhopadhyay
Learning II: Challenging Domains and Problems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1081)

Abstract

A nonstationary environment is one in which the suitability of the strategies available to a learning element changes with time. Since the optimal action in such a case is not fixed, the learning problem (i.e., the determination of the optimal strategy) becomes considerably difficult. In this paper, a two-level approach is presented for a learning automaton operating in a nonstationary environment. The lower level consists of a standard absolutely expedient learning algorithm for stationary environments. The higher level on the other hand is a tracking algorithm, based on Bayesian decision theory, for detecting changes in the environment and reinitializing the lower level algorithm in a suitable manner. Simulation studies empirically demonstrate the clear superiority of the two-level approach over the single-level learning in nonstationary environments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Wai Lam
    • 1
  • Snehasis Mukhopadhyay
    • 2
  1. 1.Department of Management SciencesThe University of IowaIowa CityUSA
  2. 2.Department of Computer and Information SciencePurdue School of Science at IndianapolisIndianapolisUSA

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