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The formalization of syntax-based mathematical algorithms using quotation and evaluation. (English) Zbl 1390.68778
Carette, Jacques (ed.) et al., Intelligent computer mathematics. MKM, Calculemus, DML, and systems and projects 2013, held as part of CICM 2013, Bath, UK, July 8–12, 2013. Proceedings. Berlin: Springer (ISBN 978-3-642-39319-8/pbk). Lecture Notes in Computer Science 7961. Lecture Notes in Artificial Intelligence, 35-50 (2013).
Summary: Algorithms like those for differentiating functional expressions manipulate the syntactic structure of mathematical expressions in a mathematically meaningful way. A formalization of such an algorithm should include a specification of its computational behavior, a specification of its mathematical meaning, and a mechanism for applying the algorithm to actual expressions. Achieving these goals requires the ability to integrate reasoning about the syntax of the expressions with reasoning about what the expressions mean. A syntax framework is a mathematical structure that is an abstract model for a syntax reasoning system. It contains a mapping of expressions to syntactic values that represent the syntactic structures of the expressions; a language for reasoning about syntactic values; a quotation mechanism to refer to the syntactic value of an expression; and an evaluation mechanism to refer to the value of the expression represented by a syntactic value. We present and compare two approaches, based on instances of a syntax framework, to formalize a syntax-based mathematical algorithm in a formal theory \(T\). In the first approach the syntactic values for the expressions manipulated by the algorithm are members of an inductive type in \(T\), but quotation and evaluation are functions defined in the metatheory of \(T\). In the second approach every expression in \(T\) is represented by a syntactic value, and quotation and evaluation are operators in \(T\) itself.
For the entire collection see [Zbl 1268.68008].

MSC:
68W30 Symbolic computation and algebraic computation
68T30 Knowledge representation
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