Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Principles of artificial intelligence
Principles of artificial intelligence
The role of explicit contextual knowledge in learning concepts to improve performance
The role of explicit contextual knowledge in learning concepts to improve performance
On Effective Procedures for Speeding Up Algorithms
Journal of the ACM (JACM)
A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Shift of bias for inductive concept learning
Shift of bias for inductive concept learning
The Knowledge Engineering Review
Explanation-based generalization of partially ordered plans
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
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Previous work in explanation-based learning has primarily focused on developing problem solvers that learn by observing solutions. However, learning from solutions is only one strategy for improving performance. This paper describes how the PRODIGY system uses explanation-based specialization to learn from a variety of phenomena, including solutions, failures, and goal-interactions. Explicit target concepts describe these phenomena, and each target concept is associated with a strategy for dynamically improving the performance of the problem solver. Explanations are formulated using a theory describing the domain and the PRODIGY problem solver. Both the target concepts and the theory are declaratively specified and extensible.