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arXiv · 2011.14246

Monte Carlo Tree Search for a single target search game on a 2-D lattice

Elana Kozak·Scott Hottovy
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Abstract

Monte Carlo Tree Search (MCTS) is a branch of stochastic modeling that utilizes decision trees for optimization, mostly applied to artificial intelligence (AI) game players. This project imagines a game in which an AI player searches for a stationary target within a 2-D lattice. We analyze its behavior with different target distributions and compare its efficiency to the Levy Flight Search, a model for animal foraging behavior. In addition to simulated data analysis we prove two theorems about the convergence of MCTS when computation constraints neglected.

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Elana Kozak, Scott Hottovy. 2020-11-29. Monte Carlo Tree Search for a single target search game on a 2-D lattice. https://arxiv.org/abs/2011.14246

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