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Monte Carlo Action Programming

Artificial Intelligence 2017-03-01 v1 Programming Languages
Authors: Lenz Belzner
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Abstract

This paper proposes Monte Carlo Action Programming, a programming language framework for autonomous systems that act in large probabilistic state spaces with high branching factors. It comprises formal syntax and semantics of a nondeterministic action programming language. The language is interpreted stochastically via Monte Carlo Tree Search. Effectiveness of the approach is shown empirically.

Keywords

monte carlo tree search programming languages program analysis

Cite

@article{arxiv.1702.08441,
  title  = {Monte Carlo Action Programming},
  author = {Lenz Belzner},
  journal= {arXiv preprint arXiv:1702.08441},
  year   = {2017}
}

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