Relational Local Iterative Compression
2010
Orseau , Laurent (INRA , Paris (France). UMR 0518 Mathématiques et Informatique Appliquées) | Eric Baum (Editeur) | Marcus Hutter (Editeur) | Emanuel Kitzelmann (Editeur)
Compression in the program space is of high importance in Artificial General Intelligence. Since maximal data compression in the general sense is not possible to achieve, it is necessary to use approximate algorithms, like AIXIt;l. This paper introduces a system that is able to compress data locally and iteratively, in a relational description language. The system thus belongs to the anytime algorithm family: the more time spent, the better it performs. The locality property is also well-suited for AGI agents to allow them to focus on "interesting" parts of the data.
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