Communications of the ACM
Machine learning: an integrated framework and its applications
Machine learning: an integrated framework and its applications
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The Utility of Knowledge in Inductive Learning
Machine Learning
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Exploiting the deep structure of constraint problems
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Artificial Intelligence
Learning first order universal Horn expressions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The constrainedness knife-edge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An introduction to database systems (7th ed.)
An introduction to database systems (7th ed.)
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Machine 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
Machine Learning
ENIGMA: A System That Learns Diagnostic Knowledge
IEEE Transactions on Knowledge and Data Engineering
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Analyzing Relational Learning in the Phase Transition Framework
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Experimental Study of Phase Transitions in Matching
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
C4.5 competence map: a phase transition-inspired approach
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Randomised restarted search in ILP
Machine Learning
Learning Horn Expressions with LOGAN-H
The Journal of Machine Learning Research
QG/GA: a stochastic search for Progol
Machine Learning
A note on phase transitions and computational pitfalls of learning from sequences
Journal of Intelligent Information Systems
On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate
Inductive Logic Programming
QG/GA: A Stochastic Search for Progol
Inductive Logic Programming
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
ILP Through Propositionalization and Stochastic k-Term DNF Learning
Inductive Logic Programming
A Model to Study Phase Transition and Plateaus in Relational Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Parallel ILP for distributed-memory architectures
Machine Learning
Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Phase transitions within grammatical inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relational random forests based on random relational rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A phase transition-based perspective on multiple instance kernels
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
An overview of AI research in Italy
Artificial intelligence
ProGolem: a system based on relative minimal generalisation
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning discriminant rules as a minimal saturation search
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
When does it pay off to use sophisticated entailment engines in ILP?
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Guiding the search in the NO region of the phase transition problem with a partial subsumption test
ECML'06 Proceedings of the 17th European conference on Machine Learning
Learning theories using estimation distribution algorithms and (reduced) bottom clauses
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Machine learning strongly relies on the covering test to assess whether a candidate hypothesis covers training examples. The present paper investigates learning relational concepts from examples, termed relational learning or inductive logic programming. In particular, it investigates the chances of success and the computational cost of relational learning, which appears to be severely affected by the presence of a phase transition in the covering test. To this aim, three up-to-date relational learners have been applied to a wide range of artificial, fully relational learning problems. A first experimental observation is that the phase transition behaves as an attractor for relational learning; no matter which region the learning problem belongs to, all three learners produce hypotheses lying within or close to the phase transition region. Second, a failure region appears. All three learners fail to learn any accurate hypothesis in this region. Quite surprisingly, the probability of failure does not systematically increase with the size of the underlying target concept: under some circumstances, longer concepts may be easier to accurately approximate than shorter ones. Some interpretations for these findings are proposed and discussed.