Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Induction of relational productions in the presence of background information
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Online structure learning for Markov logic networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
ECML'05 Proceedings of the 16th European conference on Machine Learning
Random walk inference and learning in a large scale knowledge base
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Generative structure learning for Markov logic networks based on graph of predicates
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Lifted online training of relational models with stochastic gradient methods
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and crossing local plateaus. We present our algorithm and provide learning results in two domains: family relationships and qualitative model building.