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In this contribution we propose a variant of a resolution theorem prover which selects resolution steps based on the proportion of models a newly generated clause satisfies compared to all models given in a reference class, which is generated from a subset of the initial clause set. Since the empty clause does not satisfy any models preference is given to(More)
Proof planning is a form of theorem proving in which the proving procedure is viewed as a planning process. The plan operators in proof planning are called methods. In this paper we propose a strategy for heuristically restricting the set of methods to be applied in proof search. It is based on the idea that the plausibility of a method can be estimated by(More)
In this contribution we present a variant of a resolution theorem prover which selects resolution steps based on the proportion of models a newly generated clause satisfies compared to all models given in a reference class. This reference class is generated from a subset of the initial clause set. Since the empty clause does not satisfy any models ,(More)
Proof planning is an application of AI-planning in mathematical domains. The planning operators, called methods, encode proving steps. One of the strength of proof planning comes from the usage of mathematical knowledge that heuristically restricts the search space. Semantically guided proof planning takes a diierent perspective and uses semantic(More)
Proof planning is an application of AI-planning in mathematical domains. The planning operators, called methods, encode proving steps. One of the strength of proof planning comes from the usage of mathematical knowledge that heuristically restricts the search space. Semantically guided proof planning uses semantic information as search control heuristics.(More)
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