Monte Carlo Action Programming

This addresses the challenge of programming autonomous systems for complex, uncertain environments, though it appears incremental as it combines existing methods like action programming and Monte Carlo Tree Search.

The paper tackles the problem of enabling autonomous systems to act in large probabilistic state spaces with high branching factors by proposing Monte Carlo Action Programming, a programming language framework that integrates formal syntax and semantics with stochastic interpretation via Monte Carlo Tree Search, and demonstrates its effectiveness empirically.