Maximizing the chance of winning in searching Go game trees

The current global selective search and decomposition search in Go typically back up territory scores. This approach is inherently flawed. We propose a new strategy of backing up “chance of winning”. We show how an evaluation function on the chance of winning can be constructed. Also we develop a probabilistic combinatorial game model and an algorithm for decomposition search to work with probabilistic outcomes in maximizing the chance of winning.