MONTE-CARLO EXPRESSION DISCOVERY | Zendy

Tristan Cazenave | Zendy

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MONTE-CARLO EXPRESSION DISCOVERY

Author(s) -

Tristan Cazenave

Publication year - 2012

Publication title -

international journal of artificial intelligence tools

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.279

H-Index - 30

eISSN - 1793-6349

pISSN - 0218-2130

DOI - 10.1142/s0218213012500352

Subject(s) - monte carlo tree search , computer science , monte carlo method , benchmark (surveying) , tree (set theory) , genetic programming , quasi monte carlo method , binary expression tree , mathematical optimization , hybrid monte carlo , algorithm , artificial intelligence , markov chain monte carlo , mathematics , tree traversal , statistics , geodesy , geography , mathematical analysis , bayesian probability

International audienceMonte-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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