Understanding Sampling Style Adversarial Search Methods
This work addresses the limited understanding of UCT's success beyond specific domains like Go, which is an incremental analysis for adversarial search researchers.
The paper analyzes the UCT algorithm's potential in domain-independent settings and the impact of enhancing random playouts with informed playouts between weak minimax players, using synthetic game trees for empirical and analytical insights.
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.