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

URL: http://arxiv.org/abs/2011.14246v1
Date: Sun, 29 Nov 2020 01:07:45 GMT
Title: Monte Carlo Tree Search for a single target search game on a 2-D lattice
Authors: Elana Kozak and Scott Hottovy
Abstract summary: 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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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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