Logical settings for concept-learning
Artificial Intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Two-Step Model of Pattern Acquisition: Application to Tsume-Go
CG '98 Proceedings of the First International Conference on Computers and Games
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Neural Network Program of Tsume-Go
CG '98 Proceedings of the First International Conference on Computers and Games
Top-down induction of first-order logical decision trees
Artificial Intelligence
All normalized anti-monotonic overlap graph measures are bounded
Data Mining and Knowledge Discovery
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In Go, an important factor that hinders search is the large branching factor, even in local problems. Human players are strong at recognizing frequently occurring shapes and vital points. This allows them to select the most promising moves and to prune the search tree. In this paper we argue that many of these shapes can be represented as relational concepts. We present an application of the relational learner TILDE in which we learn a heuristic that gives values to candidate-moves in tsume-go (life and death) problems. Such a heuristic can be used to limit the number of evaluated moves. Even if all moves are evaluated, alpha-beta search can be sped up considerably when the candidate-moves are approximately ordered from good to bad.We validate our approach with experiments and analysis.