Pruning Nodes in the Alpha-Beta Method Using Inductive Logic Programming
نویسندگان
چکیده
This paper reports a preliminary research of application of inductive logic programming for pruning nodes in the alpha-beta gametree search method, which nds an appropriate move by looking game-trees in a limited depth ahead. The alpha-beta method reduces the number of nodes by keeping and updating lower and upper bounds of static evaluation. Pruning e ect depends on an accidental order of nodes visited, because we can expect large pruning after we have large update. This paper proposes a method to learn rules to sort nodes to yield e ective pruning, by using inductive logic programming framework. The method induces a logic program of a binary relation among nodes, and sorts nodes based on the relation. We inspected the method with the game othello.
منابع مشابه
Alpha-Beta Pruning for Games with Simultaneous Moves
Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. In this paper we introduce an Alpha-Beta-like sound pruning method for the more general class of “stacked matrix games” that allow for simultaneous moves by both players. This is accomplished by maintaining upper and lower boun...
متن کاملTop-Down Pruning in Relational Learning
Pruning is an eeective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the eld of Inductive Logic Programming. However, it has been shown that these methods can be very ineecient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these...
متن کامل2 Some Problems with Reduced Error
This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably eeciency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more eecient than alternative algorithms , along with a slight gain in accuracy. H...
متن کاملPruning Hypothesis Spaces Using Learned Domain Theories
We present a method that uses learned domain theories to speed up hypothesis search in inductive logic programming. The key insight exploited in this work is that hypotheses can be equivalent, relative to some domain theory, even if they are not isomorphic. To detect such equivalences we use learned domain theories to saturate the hypotheses and then check if these saturations become isomorphic...
متن کاملFeature Selection as Retrospective Pruning in Hierarchical Clustering
Although feature selection is a central problem in inductive learning as suggested by the growing amount of research in this area, most of the work has been carried out under the supervised learning paradigm, paying little attention to unsupervised learning tasks and, particularly, clustering tasks. In this paper, we analyze the particular beneets that feature selection may provide in hierarchi...
متن کامل