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      Towards heuristic algorithmic memory

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      Author
      Özkural, Eray
      Date
      2011
      Source Title
      Artificial General Intelligence
      Print ISSN
      0302-9743
      Publisher
      Springer, Berlin, Heidelberg
      Volume
      6830
      Pages
      382 - 387
      Language
      English
      Type
      Conference Paper
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      Abstract
      We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We introduce four synergistic update algorithms that use a Stochastic Context-Free Grammar as a guiding probability distribution of programs. The update algorithms accomplish adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. A controlled experiment with a long training sequence shows that our incremental learning approach is effective. © 2011 Springer-Verlag Berlin Heidelberg.
      Keywords
      Controlled experiment
      General Intelligence
      Incremental learning
      Learning programming
      Long term memory
      Machine learning methods
      Stochastic context free grammar
      Subprograms
      Training sequences
      Algorithms
      Context free grammars
      Learning systems
      Probability distributions
      Heuristic methods
      Permalink
      http://hdl.handle.net/11693/28377
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/978-3-642-22887-2_47
      https://doi.org/10.1007/978-3-642-22887-2
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