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Parameter learning from stochastic teachers and stochastic compulsive liars.

B John Oommen1, Govindachari Raghunath, Benjamin Kuipers

  • 1School of Computer Science, Carleton University, Ottawa, ON, Canada. oommen@scs.carleton.ca

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|August 15, 2006
PubMed
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This study introduces a new learning strategy for robots and algorithms to find unknown parameters in uncertain environments. The CPL-AdS method efficiently narrows down the search space, even when the feedback source is unreliable or deceptive.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Control Theory

Background:

  • Traditional learning automata (LA) models assume optimal action selection from an environment.
  • This paper addresses learning a parameter within an interval, interacting with a stochastic environment.
  • The environment may provide feedback with a probability p, being informative (p > 0.5) or deceptive (p < 0.5).

Purpose of the Study:

  • To develop a novel learning strategy for a mechanism to learn an unknown parameter in a closed interval.
  • To enable learning from both stochastic teachers and stochastic compulsive liars.
  • To ensure the strategy works effectively even when the mechanism is unaware of the environment's feedback accuracy.

Main Methods:

  • The proposed CPL-AdS algorithm partitions the search interval into subintervals.

Related Experiment Videos

  • It utilizes fast-converging E-optimal LRI LA to evaluate the unknown point's location.
  • The search space is pruned iteratively by eliminating at least one partition.

    • Main Results:

    • The CPL-AdS algorithm provably converges to the unknown point with desired accuracy.
    • It achieves high probability of success, approaching unity.
    • Experimental results confirm fast and accurate convergence across various feedback accuracy (p) values.

    Conclusions:

    • The CPL-AdS strategy offers a robust solution for parameter learning in uncertain and potentially deceptive environments.
    • This is the first reported method applicable to learning from stochastic compulsive liars.
    • The algorithm's adaptability and proven convergence suggest numerous potential applications.