Towards heuristic algorithmic memory
Author
Özkural, Eray
Date
2011Source Title
Artificial General Intelligence
Print ISSN
0302-9743
Publisher
Springer, Berlin, Heidelberg
Volume
6830
Pages
382 - 387
Language
English
Type
Conference PaperItem Usage Stats
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Show full item recordAbstract
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 experimentGeneral 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
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http://hdl.handle.net/11693/28377Published Version (Please cite this version)
http://dx.doi.org/10.1007/978-3-642-22887-2_47https://doi.org/10.1007/978-3-642-22887-2