By Petra Hofstedt (auth.)

Programming languages are frequently labeled in accordance with their paradigms, e.g. central, practical, common sense, constraint-based, object-oriented, or aspect-oriented. A paradigm characterizes the fashion, recommendations, and strategies of the language for describing events and approaches and for fixing difficulties, and every paradigm serves top for programming specifically program components. Real-world difficulties, even if, are frequently top applied via a mixture of techniques from varied paradigms, simply because they contain facets from numerous geographical regions, and this mixture is extra very easily discovered utilizing multiparadigm programming languages.

This booklet offers with the idea and perform of multiparadigm constraint programming languages. the writer first elaborates on programming paradigms and languages, constraints, and the merging of programming techniques which yields multiparadigm (constraint) programming languages. within the moment half the writer inspects concrete techniques on multiparadigm constraint programming – the concurrent constraint useful language CCFL, which mixes the practical and the constraint-based paradigms and permits the outline of concurrent techniques; and a normal framework for multiparadigm constraint programming and its implementation, Meta-S.

The e-book is suitable for researchers and graduate scholars within the components of programming and synthetic intelligence.

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Extra resources for Multiparadigm Constraint Programming Languages

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1 Linear arithmetic constraints As a first example we consider linear arithmetic constraints. They allow efficient handling of linear equations, inequalities, and disequations on real numbers. g. [54, 106]). Let ζRlin be a constraint system with ζRlin = (ΣR , DR , TRlin , XR , ConsRlin ). The signature ΣR contains constant symbols for the real numbers, binary function symbols +, −, ∗, and /, and the binary predicate symbols =, >, <, ≤, ≥, and =. ΣR = (SR , FR , RR ) = ({real}, {0, 1, −1, 2, . .

Of resolution steps. An SLD-refutation of (P, G) is a finite SLD-derivation G →σ1 ,C1 G1 →σ2 ,C2 G2 → . . →σn ,Cn Gn of (P, G), where Gn = . An SLD-refutation of a goal generates substitutions whose composition is called a computed answer. Definition 21 (computed answer) Let P be a logic program, let G be a goal. Let G →σ1 ,C1 G1 →σ2 ,C2 G2 → . . →σn ,Cn be an SLD-refutation. The substitution σ = σ |var(G) , where σ = σn ◦ σn−1 ◦ . . ◦ σ1 , is a computed answer of (P, G). Example 11 Consider the append program from Example 10.

We are, thus, interested in the functionalities in which constraints may act as language elements. We mainly distinguish three categories: (1) abstraction over a number of basic language constraints, (2) stating properties of objects and (3) checking or questioning of conditions. We discuss the three aspects in the following. g. as built-in constraints or by constraint libraries. However, to write compact and readable programs in a convenient way, it is often desirable to summarize or abstract over sets of constraints, just as functions abstract over statements.

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