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Experiments with Proof Plans for Induction
 Journal of Automated Reasoning
, 1992
"... The technique of proof plans, is explained. This technique is used to guide automatic inference in order to avoid a combinatorial explosion. Empirical research is described to test this technique in the domain of theorem proving by mathematical induction. Heuristics, adapted from the work of Boye ..."
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Cited by 95 (33 self)
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The technique of proof plans, is explained. This technique is used to guide automatic inference in order to avoid a combinatorial explosion. Empirical research is described to test this technique in the domain of theorem proving by mathematical induction. Heuristics, adapted from the work of Boyer and Moore, have been implemented as Prolog programs, called tactics, and used to guide an inductive proof checker, Oyster. These tactics have been partially specified in a metalogic, and the plan formation program, clam, has been used to reason with these specifications and form plans. These plans are then executed by running their associated tactics and, hence, performing an Oyster proof. Results are presented of the use of this technique on a number of standard theorems from the literature. Searching in the planning space is shown to be considerably cheaper than searching directly in Oyster's search space. The success rate on the standard theorems is high. Keywords Theorem prov...
KITP93: An Automated Inference System for Program Analysis
 Proceedings of 12th Conference on Automated Deduction
, 1994
"... This report describes KITP93 from a user's perspective. 1 Logical Framework ..."
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Cited by 1 (1 self)
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This report describes KITP93 from a user's perspective. 1 Logical Framework
Search Algorithms in the Situation Calculus
"... . In this paper we consider axiomatizing AI search algorithms in the situation calculus. Proper axiomatization of such algorithms is important for understanding control knowledge which is by definition search algorithm dependent: a control knowledge can be effective with respect to one search algori ..."
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. In this paper we consider axiomatizing AI search algorithms in the situation calculus. Proper axiomatization of such algorithms is important for understanding control knowledge which is by definition search algorithm dependent: a control knowledge can be effective with respect to one search algorithm but ineffective with respect to another. The key idea here is to view search algorithms as strict linear orders on sets of situations in the situation calculus. There are several potential advantages of viewing search algorithms this way. One is that according to it, implementing a search strategy amounts to computing the "next" relation of the corresponding linear order. We found this perspective particularly helpful in understanding various memorybounded implementations of breadthfirst and bestfirst search as it separates the definition of a search strategy from its implementations. A more important advantage is that according to it, a particularly simple account of search pruning i...