Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
Multi Level Knowledge in Modeling Qualitative PhysicsLearning
Machine Learning - Special issue on multistrategy learning
Maximizing Theory Accuracy Through Selective Reinterpretation
Machine Learning
Integrated Architectures for Machine Learning
Machine Learning and Its Applications, Advanced Lectures
Handbook of data mining and knowledge discovery
Search-intensive concept induction
Evolutionary Computation
Tractability of theory patching
Journal of Artificial Intelligence Research
Computer-aided tracing of children's physics learning: a teacher oriented view
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Toward robust real-world inference: a new perspective on explanation-based learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Hi-index | 0.00 |
This article presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a priori knowledge consists of a causal model of the domain that states the relationships among basic phenomena, and a body of phenomenological theory that describes the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively, and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and a small number of examples. The system works in a first-order logic environment and has been applied in a real domain.