Explanation-based learning: a problem solving perspective
Artificial Intelligence
Why EBL produces overly-specific knowledge: a critique of the PRODIGY approaches
ML92 Proceedings of the ninth international workshop on Machine learning
Acquiring search-control knowledge via static analysis
Artificial Intelligence
Small is beautiful: a brute-force approach to learning first-order formulas
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On-Line Learning from Search Failures
Machine Learning
Learning explanation-based search control rules for partial order planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On efficient approaches to the utility problem in adaptive problem solving
On efficient approaches to the utility problem in adaptive problem solving
Multimodal reasoning for automatic model construction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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In this paper, we consider the role that domain-dependent control knowledge plays in problem solving systems. Ginsberg and Geddis (Ginsberg & Geddis 1991) have claimed that domain-dependent control information has no place in declarative systems; instead, they say, such information should be derived from declarative facts about the domain plus domain-independent principles. We dispute their conclusion, arguing that it is impractical to generate control knowledge solely on the basis of logical derivations. We propose that simplifying abstractions are crucial for deriving control knowledge, and, as a result, empirical utility evaluation of the resulting rules will frequently be necessary to validate the utility of derived control knowledge. We illustrate our arguments with examples from two implemented systems.