An older FORTRAN program is restructured to a style that is related to data abstraction and object‐oriented programming. The restructuring process, which can be applied to many older procedural languages, is driven by goals that are related to clarity of function. Possible benefits include maintainability and a better understanding of the original program.
Proof planning is an application of AI planning to theorem proving that employs plan operators that encapsulate mathematical proof techniques. Many proofs require the instantiation of variables; that is, mathematical objects with certain properties have to be constructed. This is particularly difficult for automated theorem provers if the instantiations have to satisfy requirements specific for a mathematical theory, for example, for finite sets or for real numbers, because in this case unification is insufficient for finding a proper instantiation. Often, constraint solving can be employed for this task. We describe a framework for the integration of constraint solving into proof planning that combines proof planners and stand-alone constraint solvers. Proof planning has some peculiar requirements that are not met by any off-the-shelf constraint-solving system. Therefore, we extended an existing propagation-based constraint solver in a generic way. This approach generalizes previous work on tackling the problem. It provides a more principled way and employs existing AI technology.
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