Abstract

In this paper, a three-component architecture of a learning environment for Go is sketched, which can be applied to any two-player, deterministic, full information, partizan, combinatorial game. The architecture called HUGO has natural and human-like reasoning components. Its most abstract component deals with the selection of subgames of Go. The second component is concerned with initiative. The notion of gote no sente (a move that loses initiative but creates new lines of play that will hold initiative) is formalized. In the third component, game values are computed with a new kind of alpha-beta algorithm based on fuzzy, partial ordering. Our approach leaves some valuable control parameters and others ways to apply further machine learning techniques.