DeepAI AI Chat
Log In Sign Up

Understanding Sampling Style Adversarial Search Methods

by   Raghuram Ramanujan, et al.

UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been particularly successful in the game of Go but the reasons for its success are not well understood and attempts to replicate its success in other domains such as Chess have failed. We provide an in-depth analysis of the potential of UCT in domain-independent settings, in cases where heuristic values are available, and the effect of enhancing random playouts to more informed playouts between two weak minimax players. To provide further insights, we develop synthetic game tree instances and discuss interesting properties of UCT, both empirically and analytically.


Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Monte Carlo Tree Search (MCTS) has improved the performance of game engi...

Monte Carlo Methods for the Game Kingdomino

Kingdomino is introduced as an interesting game for studying game playin...

Lookahead Pathology in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that fi...

Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning

Balancing exploration and exploitation has been an important problem in ...