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Monte Carlo Action Programming
February 25, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Lenz Belzner
arXiv ID
1702.08441
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.PL
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
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.
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