• Stefan Wrobel


At the end of this book, we want to conclude with an assessment of areas where we hope this book will make a contribution, a description of problems that are still open, and a discussion of possible future work.


Reinforcement Learning Concept Formation Action Selection Inductive Logic Programming Concept Hierarchy 
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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  1. 1.
    As pointed out in [Wrobel, 1991], a possible choice for action selection would be a hierarchy of situation-action descriptions into which a new situation is classified.Google Scholar
  2. 2.
    The initial vocabulary could either be a maximally coarse, i.e., binary segmentation, or a specific pre-determined segmentation that the agent could have inherited through evolution-like processes. This opens up the interesting possibility of using genetic algorithms [DeJong, 1988] to study the development of elementary feature sets.Google Scholar
  3. 3.
    A distinction is non-informative if the conditional probability of a (predicted) effector value given an attribute value is identical or nearly identical for both values. If a probabilistic concept hierarchy is used, this can be computed from the attribute probabilities stored with the concepts.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  • Stefan Wrobel
    • 1
  1. 1.GMDSankt AugustinGermany

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