Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Rippling: a heuristic for guiding inductive proofs
Artificial Intelligence
Universal classical planner: an algorithm for unifying state-space and plan-space planning
New directions in AI planning
Planning from second principles
Artificial Intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
The Heine–Borel Challenge Problem. In Honor of Woody Bledsoe
Journal of Automated Reasoning
Analogy in Inductive Theorem Proving
Journal of Automated Reasoning
Case-Based Reasoning Technology, From Foundations to Applications
Omega: Towards a Mathematical Assistant
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
A model of analogy-driven proof-plan construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Second-order matching modulo evaluation: a technique for reusing proofs
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Proof Planning with Multiple Strategies
CL '00 Proceedings of the First International Conference on Computational Logic
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We discuss several problems of analogy-driven proof plan construction which prevent a solution for more difficult target problems or make a solution very expensive. Some of these problems are due to the previously assumed fixed order of matching, reformulation, and replay in case-based reasoning and from a too restricted combination of planning from first principles with the analogy process. In order to overcome these problems we suggest to interleave matching and replay as well as casebased planning with planning from first principles. Secondly, the restricted mixture of case-based planning and planning from first principles in previous systems is generalised to intelligently employing different planning strategies with the objective to solve more problems at all and to solve problems more efficiently.