Monte Carlo Tree Search for a single target search game on a 2-D lattice
This research provides insights into the applicability and efficiency of MCTS for search problems, potentially benefiting AI game players and search algorithms in general.
This paper investigates Monte Carlo Tree Search (MCTS) for locating a stationary target on a 2-D lattice, comparing its efficiency against Levy Flight Search under various target distributions. The study includes simulated data analysis and theoretical proofs regarding MCTS convergence when computational constraints are ignored.
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.