The computer modelling of mathematical reasoning
The computer modelling of mathematical reasoning
A Proof Procedure Using Connection Graphs
Journal of the ACM (JACM)
Guarded commands, nondeterminacy and formal derivation of programs
Communications of the ACM
Extraction and generalization of expert advice (learning, representation, induction)
Extraction and generalization of expert advice (learning, representation, induction)
LEAP: a learning apprentice for VLSI design
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Representation and use of explicit justifications for knowledge base refinements
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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One of the major 'weaknesses of current automated reasoning systems is that they lack the ability to control inference in a sophisticated, context-directed fashion. General strategies such as the set-of-support strategy are useful, but have proven inadequate for many individual problems. A strategy component is needed that possesses knowledge about many particular domains and problems. Such a body of knowledge would require a prohibitive amount of time to construct by hand. This leads us to consider means of automatically acquiring control knowledge from example proofs. One particular means of learning is explanation-based learning. This paper analyzes the basis of explanations -- finding weakest preconditions that enable a particular rule to fire -- to derive a representation within which explanations can be extracted from examples, generalized and used to guide the actions of a problem-solving system.