Constraint logic programming emerged in the late 80's as a highly declarative class of programming languages based on first-order logic and theories with decidable constraint languages, thereby subsuming Prolog re...
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ISBN:
(纸本)9789819722990;9789819723003
Constraint logic programming emerged in the late 80's as a highly declarative class of programming languages based on first-order logic and theories with decidable constraint languages, thereby subsuming Prolog restricted to equality constraints over the Herbrand's term domain. This approach has proven extremely successful in solving combinatorial problems in the industry which quickly led to the development of a variety of constraint solving libraries in standard programming languages. Later came the design of a purely declarative front-end constraint-based modeling language, MiniZinc, independent of the constraint solvers, in order to compare their performances and create model benchmarks. Beyond that purpose, the use of a high-level modeling language such as MiniZinc to develop complete applications, or to teach constraint programming, is limited by the impossibility to program search strategies, or new constraint solvers, in a modeling language, as well as by the absence of an integrated development environment for both levels of constraint-based modeling and constraint solving. In this paper, we propose to solve those issues by taking Prolog with its constraint solving libraries, as a unified relation-based modeling and programming language. We present a Prolog library for high-level constraint-based mathematical modeling, inspired by MiniZinc, using subscripted variables (arrays) in addition to lists and terms, quantifiers and iterators in addition to recursion, together with a patch of constraint libraries in order to allow array functional notations in constraints. We show that this approach does not come with a significant computation time overhead, and presents several advantages in terms of the possibility of focussing on mathematical modeling, getting answer constraints in addition to ground solutions, programming search or constraint solvers if needed, and debugging models within a unique modeling and programming environment.
The attributed variable data type plays an important role in many extensions to the basic Logic Programming language. It provides a flexible mechanism to implement constraint solvers over different domains in a standa...
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The attributed variable data type plays an important role in many extensions to the basic Logic Programming language. It provides a flexible mechanism to implement constraint solvers over different domains in a standard logic programming system. To combine the technique of constraint solving with tabling and build a tabled constraint logic programming system, special work has to be done to handle attributed variables when they are copied into and out of tables. In this paper, we describe the implementation of attributed variables in XSB — a tabled logic programming system. We first introduce the internal representation of attributed variables and the interface between the internal representation and the user defined high level unification handler. Then, as the primary focus of the paper, we describe how the tabling mechanism in XSB can be extended to efficiently handle attributed variables. To save attributed variables in tables, the structure of subgoal table and answer table is modified, and the tabling engine itself requires extension as well.
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