Abstract
Algorithm design systems are sensitive to the formulation of a problem specification and the domain theory in which they reason. In this paper we argue that the performance of an algorithm design system can be enhanced by first reformulating a problem specification into an abstract data type by incorporating relevant problem properties. We will describe the semantics of a class of valid problem reformulations and relate them to the semantics of the implementation relation between models. Abstraction reformulations are the inverse of implementation reformulations. We will describe STRATA, an automatic problem reformulation system, which generates abstract data types from a problem specification and a domain theory specified in equational logic. STRATA reasons from the first principles of universal algebra.
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© 1990 Kluwer Academic Publishers
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Lowry, M.R. (1990). STRATA: Problem Reformulation and Abstract Data Types. In: Benjamin, D.P. (eds) Change of Representation and Inductive Bias. The Kluwer International Series in Engineering and Computer Science, vol 87. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1523-0_3
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DOI: https://doi.org/10.1007/978-1-4613-1523-0_3
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