Communications of the ACM
Algorithmics: theory & practice
Algorithmics: theory & practice
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Inductive logic programming and learnability
ACM SIGART Bulletin
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
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Declarative Bias for Specific-to-General ILP Systems
Machine Learning - Special issue on bias evaluation and selection
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
Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy learning
On the conversion between non-binary constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Local search for statistical counting
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The constrainedness knife-edge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Learning Logical Definitions from Relations
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
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Summarizing CSP hardness with continuous probability distributions
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Approximate resolution of hard numbering problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Phase Transitions and Stochastic Local Search in k-Term DNF Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Abstraction and Phase Transitions in Relational Learning
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Abstracting Visual Percepts to Learn Concepts
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Can Relational Learning Scale Up?
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Theta-Subsumption in a Constraint Satisfaction Perspective
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
A Monte Carlo Approach to Hard Relational Learning Problems
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
DS '01 Proceedings of the 4th International Conference on Discovery Science
Perceptual Learning and Abstraction in Machine Learning
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Relational learning as search in a critical region
The Journal of Machine Learning Research
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
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
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
A note on phase transitions and computational pitfalls of learning from sequences
Journal of Intelligent Information Systems
A Model to Study Phase Transition and Plateaus in Relational Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Learning on the phase transition edge
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Phase transitions within grammatical inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Propositionalization for clustering symbolic relational descriptions
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Exploiting propositionalization based on random relational rules for semi-supervised learning
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
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
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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One of the major limitations of relational learning is due to the complexity of verifying hypotheses on examples. In this paper we investigate this task in light of recent published results, which show that many hard problems exhibit a narrow “phase transition” with respect to some order parameter, coupled with a large increase in computational complexity. First we show that matching a class of artificially generated Horn clauses on ground instances presents a typical phase transition in solvability with respect to both the number of literals in the clause and the number of constants occurring in the instance to match. Then, we demonstrate that phase transitions also appear in real-world learning problems, and that learners tend to generate inductive hypotheses lying exactly on the phase transition. On the other hand, an extensive experimenting revealed that not every matching problem inside the phase transition region is intractable. However, unfortunately, identifying those that are feasible cannot be done solely on the basis of the order parameters. To face this problem, we propose a method, based on a Monte Carlo algorithm, to estimate on-line the likelihood that the current matching problem will exceed a given amount of computational resources. The impact of the above findings on relational learning is discussed.