Advertisement

Using Analogy Discovery to Create Abstractions

  • Marc Pickett
Conference paper
  • 428 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)

Abstract

Concept formation is a form of abstraction that allows for knowledge transfer, generalization, and compact representation. Concepts are useful for the creation of a generally intelligent autonomous agent. If an autonomous agent is experiencing a changing world, then nearly every experience it has will be unique in that it will have at least slight differences from other experiences. Concepts allow an agent to generalize these experiences and other data. In some applications, the concepts that an agent uses are explicitly provided by a human programmer. A problem with this approach is that the agent encounters difficulties when it faces situations that the programmer had not anticipated. For this reason, it would be useful for the agent to automatically form concepts in an unsupervised setting. The agent should be able to depend as little as possible on representations tailored by humans, and therefore it should develop its own representations from raw uninterpreted data.

Keywords

Knowledge Transfer Concept Formation Minimum Description Length Chess Game Machine Learning Database 
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.

References

  1. 1.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  2. 2.
    Hawkins, J., Blakeslee, S.: On Intelligence. Times Books (2004)Google Scholar
  3. 3.
    Hofstadter, D.R.: Analogy as the core of cognition. The Analogical Mind: Perspectives from Cognitive Science, pp. 499–538 (2001)Google Scholar
  4. 4.
    Holder, L., Cook, D., Djoko, S.: Substructure discovery in the subdue system. In: Proceedings of the Workshop on Knowledge Discovery in Databases (1994)Google Scholar
  5. 5.
    Pickett, M., Barto, A.: Policyblocks: An algorithm for creating useful macro-actions in reinforcement learning. In: Proceedings of the International Conference on Machine Learning (2002)Google Scholar
  6. 6.
    Pickett, M., Oates, T.: The cruncher: Automatic concept formation using minimum description length. In: Zucker, J.-D., Saitta, L. (eds.) SARA 2005. LNCS (LNAI), vol. 3607, Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Wolff, J.G.: Information compression by multiple alignment, unification and search as a unifying principle in computing and cognition. Artif. Intell. Rev. (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marc Pickett
    • 1
  1. 1.Cognition, Robotics, and Learning, University of Maryland, Baltimore County 

Personalised recommendations