Monte Carlo Tree Search for a Search Game on a 2-D Lattice

Midshipman Researcher(s): 1/C Elana Kozak

Adviser(s): Professor Scott Hottovy

Poster #52

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 disappear.

Full Size Mathematics #52