Explanation-based generalisation = partial evaluation
Artificial Intelligence
Partial evaluation in logic programming
Journal of Logic Programming
ML92 Proceedings of the ninth international workshop on Machine learning
A structural theory of explanation-based learning
Artificial Intelligence
Tutorial on specialisation of logic programs
PEPM '93 Proceedings of the 1993 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulation
The Go¨del programming language
The Go¨del programming language
A strategic metagame player for general chess-like games
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
A Comparative Revisitation of Some Program Transformation Techniques
Selected Papers from the Internaltional Seminar on Partial Evaluation
Challenge problems for artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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When a metaprogram automatically creates rules, some created rules are useless because they can never apply. Some metarules, that we call impossibility metarules, are used to remove useless rules. Some of these metarules are general and apply to any generated program. Some are domain specific metarules. In this paper, we show how dynamic metaprogramming can be used to create domain specific impossibility metarules. Applying metaprogramming to impossibility metaprogramming avoids writing specific metaprogram for each domain metaprogramming is applied to. Our meta-metaprograms have been used to write metaprograms that write search rules for different games and planning domains. They write programs that write selective and efficient search programs.