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Monte-Carlo expression discovery

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Date
2013
Dewey
Méthodes informatiques spéciales
Sujet
UCT; nested Monte-Carlo search; expression discovery
Journal issue
International Journal on Artificial Intelligence Tools
Volume
22
Number
1
Publication date
2013
Article pages
1-22
Publisher
World Scientific
DOI
http://dx.doi.org/10.1142/S0218213012500352
URI
https://basepub.dauphine.fr/handle/123456789/11131
Collections
  • LAMSADE : Publications
Metadata
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Author
Cazenave, Tristan
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Pro-gramming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from ex-pression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize. [ABSTRACT FROM AUTHOR]

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