Monte Carlo Action Programming
Lenz Belzner
TL;DR
This paper introduces Monte Carlo Action Programming, a language framework for autonomous systems operating in complex probabilistic environments, utilizing Monte Carlo Tree Search for interpretation and demonstrating empirical effectiveness.
Contribution
It presents a formal syntax and semantics for a nondeterministic action programming language interpreted through Monte Carlo Tree Search, tailored for large probabilistic state spaces.
Findings
Empirical validation shows effectiveness of the approach.
The framework handles high branching factors efficiently.
The language's semantics support stochastic interpretation.
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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Taxonomy
TopicsArtificial Intelligence in Games · Reinforcement Learning in Robotics · Computability, Logic, AI Algorithms