Tree clustering for constraint networks (research note)
Artificial Intelligence
Decomposing constraint satisfaction problems using database techniques
Artificial Intelligence
On the efficiency of subsumption algorithms
Journal of the ACM (JACM)
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Phase Transitions in Relational Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Analyzing Relational Learning in the Phase Transition Framework
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient Theta-Subsumption Based on Graph Algorithms
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Feature term subsumption using constraint programming with basic variable symmetry
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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The covering test intensively used in Inductive Logic Programming, i.e. θ-subsumption, is formally equivalent to a Constraint Satisfaction problem (CSP). This paper presents a general reformulation of θ-subsumption into a binary CSP, and a new θ-subsumption algorithm, termed Django, which combines some main trend CSP heuristics and other heuristics specifically designed for θ-subsumption. Django is evaluated after the CSP standards, shifting from a worst-case complexity perspective to a statistical framework, centered on the notion of Phase Transition (PT). In the PT region lie the hardest on average CSP instances; and this region has been shown of utmost relevance to ILP [4]. Experiments on artificial θ-subsumption problems designed to illustrate the phase transition phenomenon, show that Django is faster by several orders of magnitude than previous θ-subsumption algorithms, within and outside the PT region.