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Статті в журналах з теми "Constraint logic"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

FAGES, FRANÇOIS, and EMMANUEL COQUERY. "Typing constraint logic programs." Theory and Practice of Logic Programming 1, no. 6 (2001): 751–77. http://dx.doi.org/10.1017/s1471068401001120.

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Анотація:

We present a prescriptive type system with parametric polymorphism and subtyping for constraint logic programs. The aim of this type system is to detect programming errors statically. It introduces a type discipline for constraint logic programs and modules, while maintaining the capabilities of performing the usual coercions between constraint domains, and of typing meta-programming predicates, thanks to the exibility of subtyping. The property of subject reduction expresses the consistency of a prescriptive type system w.r.t. the execution model: if a program is ‘well-typed’, then all deriva

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2

Van Hentenryck, Pascal. "Constraint logic programming." Knowledge Engineering Review 6, no. 3 (1991): 151–94. http://dx.doi.org/10.1017/s0269888900005798.

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Анотація:

AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from thei

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3

DUNDUA, BESIK, MÁRIO FLORIDO, TEMUR KUTSIA, and MIRCEA MARIN. "CLP(H):Constraint logic programming for hedges." Theory and Practice of Logic Programming 16, no. 2 (2015): 141–62. http://dx.doi.org/10.1017/s1471068415000071.

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Анотація:

AbstractCLP(H) is an instantiation of the general constraint logic programming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds of variables: for terms, for hedges, and for function symbols. Constraints involve equations between unranked terms and atoms for regular hedge language membership. We study algebraic semantics of CLP(H) programs, define a sound, terminating, and incomplete constraint solver, investigate two fragments of constraints for which the solver returns a complete set of solutions,

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4

DIACONESCU, RĂZVAN. "Category-based constraint logic." Mathematical Structures in Computer Science 10, no. 3 (2000): 373–407. http://dx.doi.org/10.1017/s0960129599002960.

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Анотація:

The research reported in this paper exploits the view of constraint programming as computation in a logical system, namely constraint logic. The basic ingredients of constraint logic are: constraint models for the semantics (they form a comma-category over a fixed model of ‘built-ins’); generalized polynomials in the role of basic syntactic ingredient; and a constraint satisfaction relation between semantics and syntax. Category-based constraint logic means the development of the logic is abstract categorical rather than concrete set theoretical.We show that (category-based) constraint logic i

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5

Van Hentenryck, Pascal, Helmut Simonis, and Mehmet Dincbas. "Constraint satisfaction using constraint logic programming." Artificial Intelligence 58, no. 1-3 (1992): 113–59. http://dx.doi.org/10.1016/0004-3702(92)90006-j.

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6

Cohen, Jacques. "Logic programming and constraint logic programming." ACM Computing Surveys 28, no. 1 (1996): 257–59. http://dx.doi.org/10.1145/234313.234416.

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7

FIERBINTEANU, CRISTINA. "CONSTRAINT LOGIC PROGRAMMING APPROACH OF NETWORK FLOW PROBLEMS WITHIN A DECISION SUPPORT SYSTEMS GENERATOR FOR TRANSPORTATION PLANNIG." International Journal on Artificial Intelligence Tools 07, no. 04 (1998): 453–62. http://dx.doi.org/10.1142/s0218213098000214.

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Анотація:

In this paper we propose a model of a decision support systems (DSS) generator for unstructured problems. The model is developed within the constraint logic programming (CLP) paradigm. At the center of the generator there is an ontology defining the concepts and relationships necessary and sufficient to describe the domain to be reasoned about, in a manner suitable for a particular class of tasks. The constraint solver of the constraint logic programming host language has to be extended with constraints which are relevant to the domain studied, but can not be found among the general constraint

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8

Diaconescu, Răzvan. "Completeness of category-based equational deduction." Mathematical Structures in Computer Science 5, no. 1 (1995): 9–40. http://dx.doi.org/10.1017/s0960129500000621.

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Анотація:

Equational deduction is generalised within a category-based abstract model theory framework, and proved complete under a hypothesis of quantifier projectivity, using a semantic treatment that regards quantifiers as models rather than variables, and valuations as model morphisms rather than functions. Applications include many- and order-sorted (conditional) equational logics, Horn clause logic, equational deduction modulo a theory, constraint logics, and more, as well as any possible combination among them. In the cases of equational deduction modulo a theory and of constraint logic the comple

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9

APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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Анотація:

We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we

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10

Huang, Peixin, Xiang Zhao, Minghao Hu, Zhen Tan, and Weidong Xiao. "Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24141–49. https://doi.org/10.1609/aaai.v39i23.34589.

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To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods for such problems have commonly built document-level event graphs for global reasoning across sentences. However, the edges between events are usually derived from external tools heuristically, which are not always reliable and may introduc

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11

MANCARELLA, PAOLO, GIACOMO TERRENI, FARIBA SADRI, FRANCESCA TONI, and ULLE ENDRISS. "The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments." Theory and Practice of Logic Programming 9, no. 6 (2009): 691–750. http://dx.doi.org/10.1017/s1471068409990093.

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Анотація:

AbstractWe present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state-of-the-art abductive systems and answer set solvers and showing how to use it to program some applications.

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12

Cohen, Jacques. "Constraint logic programming languages." Communications of the ACM 33, no. 7 (1990): 52–68. http://dx.doi.org/10.1145/79204.79209.

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13

Bensaou, N., and I. Guessarian. "Transforming constraint logic programs." Theoretical Computer Science 206, no. 1-2 (1998): 81–125. http://dx.doi.org/10.1016/s0304-3975(97)00077-7.

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14

Wilson, Molly, and Alan Borning. "Hierarchical constraint logic programming." Journal of Logic Programming 16, no. 3-4 (1993): 277–318. http://dx.doi.org/10.1016/0743-1066(93)90046-j.

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15

Monfroglio, A. "Neural Logic Constraint Solving." Journal of Parallel and Distributed Computing 20, no. 1 (1994): 92–98. http://dx.doi.org/10.1006/jpdc.1994.1009.

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16

FERNÁNDEZ, ANTONIO J., TERESA HORTALÁ-GONZÁLEZ, FERNANDO SÁENZ-PÉREZ, and RAFAEL DEL VADO-VÍRSEDA. "Constraint functional logic programming over finite domains." Theory and Practice of Logic Programming 7, no. 5 (2007): 537–82. http://dx.doi.org/10.1017/s1471068406002924.

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Анотація:

AbstractIn this paper, we present our proposal to Constraint Functional Logic Programming over Finite Domains (CFLP($\fd$)) with a lazy functional logic programming language which seamlessly embodies finite domain ($\fd$) constraints. This proposal increases the expressiveness and power of constraint logic programming over finite domains (CLP($\fd$)) by combining functional and relational notation, curried expressions, higher-order functions, patterns, partial applications, non-determinism, lazy evaluation, logical variables, types, domain variables, constraint composition, and finite domain c

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17

Bergenti, Federico, Stefania Monica, and Gianfranco Rossi. "Constraint Logic Programming with Polynomial Constraints over Finite Domains." Fundamenta Informaticae 161, no. 1-2 (2018): 9–27. http://dx.doi.org/10.3233/fi-2018-1693.

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18

Geibinger, Tobias, Florian Mischek, and Nysret Musliu. "Constraint Logic Programming for Real-World Test Laboratory Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 6358–66. http://dx.doi.org/10.1609/aaai.v35i7.16789.

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Анотація:

The Test Laboratory Scheduling Problem (TLSP) and its subproblem TLSP-S are real-world industrial scheduling problems that are extensions of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, TLSP includes a grouping phase where the jobs to be scheduled have to be assembled from smaller tasks and derive their properties from this grouping. For TLSP-S such a grouping is already part of the input. In this work, we show how TLSP-S can be solved by Answer-set Programming extended with ideas from other constraint solving paradigms. We propose a nove

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19

ABDENNADHER, SLIM, and CHRISTOPHE RIGOTTI. "AUTOMATIC GENERATION OF RULE-BASED SOLVERS FOR INTENTIONALLY DEFINED CONSTRAINTS." International Journal on Artificial Intelligence Tools 11, no. 02 (2002): 283–302. http://dx.doi.org/10.1142/s0218213002000903.

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A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In previous work, different methods for automatic generation of rule-based solvers for constraints defined over finite domains have been proposed1,2,3,4. In this paper, we present a method for generating rule-based solvers for constraint predicates defined by means of a constraint logic program, even when the constraint domain is infinit

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20

ZHENG, LEI, CHUNNIAN LIU, DONG JIA, and NING ZHONG. "GENERATING NUMERICAL CONSTRAINTS IN CILP." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 01 (2005): 91–108. http://dx.doi.org/10.1142/s0218001405003946.

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Анотація:

A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple

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21

Kanchanasut, Kanchana, and Peter J. Stuckey. "Transforming normal logic programs to constraint logic programs." Theoretical Computer Science 105, no. 1 (1992): 27–56. http://dx.doi.org/10.1016/0304-3975(92)90286-o.

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22

LEE, J. H. M., and V. W. L. TAM. "A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING." International Journal on Artificial Intelligence Tools 04, no. 01n02 (1995): 3–32. http://dx.doi.org/10.1142/s0218213095000024.

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Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple

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23

VOLIOTIS, C., N. M. SGOUROS, and G. PAPAKONSTANTINOU. "ATTRIBUTE GRAMMAR BASED MODELING OF CONCURRENT CONSTRAINT LOGIC PROGRAMMING." International Journal on Artificial Intelligence Tools 04, no. 03 (1995): 383–411. http://dx.doi.org/10.1142/s021821309500019x.

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The development of more powerful Concurrent Constraint Logic Programming (CCLP) languages depends largely on the development of environments that facilitate the specification and integration of constraints in the semantics of a logic program and automatically extract the inherent parallelism of Logic Programming. This paper presents a novel method for automating the parallel AND/OR execution of CCLP applications. This method consists of two stages. The first stage translates a logic program into an equivalent Attribute Grammar providing a common language in which the specifications of constrai

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24

Sitek, Pawel, and Jaroslaw Wikarek. "A Hybrid Method for the Modelling and Optimisation of Constrained Search Problems." Foundations of Management 5, no. 3 (2014): 7–22. http://dx.doi.org/10.2478/fman-2014-0016.

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Анотація:

AbstractThe paper presents a concept and the outline of the implementation of a hybrid approach to modelling and solving constrained problems. Two environments of mathematical programming (in particular, integer programming) and declarative programming (in particular, constraint logic programming) were integrated. The strengths of integer programming and constraint logic programming, in which constraints are treated in a different way and different methods are implemented, were combined to use the strengths of both. The hybrid method is not worse than either of its components used independentl

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25

Lakmazaheri, Sivand, and William J. Rasdorf. "Constraint logic programming for the analysis and partial synthesis of truss structures." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3, no. 3 (1989): 157–73. http://dx.doi.org/10.1017/s0890060400001207.

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A general constraint-based formulation for the analysis and partial synthesis of two-dimensional truss structures is presented. This formulation is general in that it handles statically determinate and statically indeterminate trusses with pin and roller supports, and concentrated joint loads. The formulation is constraint-based in that the physical behavior of truss components is declaratively represented using constraints.The analysis and partial synthesis of a truss structure manifest themselves in proving the satisfiability of the constraints associated with the structural components. An a

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26

LIU, GUOHUA, and JIA-HUAI YOU. "Relating weight constraint and aggregate programs: Semantics and representation." Theory and Practice of Logic Programming 13, no. 1 (2011): 1–31. http://dx.doi.org/10.1017/s147106841100038x.

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AbstractWeight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely, the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that f

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27

Gnecco, Giorgio, Marco Gori, Stefano Melacci, and Marcello Sanguineti. "Foundations of Support Constraint Machines." Neural Computation 27, no. 2 (2015): 388–480. http://dx.doi.org/10.1162/neco_a_00686.

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Анотація:

The mathematical foundations of a new theory for the design of intelligent agents are presented. The proposed learning paradigm is centered around the concept of constraint, representing the interactions with the environment, and the parsimony principle. The classical regularization framework of kernel machines is naturally extended to the case in which the agents interact with a richer environment, where abstract granules of knowledge, compactly described by different linguistic formalisms, can be translated into the unified notion of constraint for defining the hypothesis set. Constrained va

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28

Hoogeboom, Hendrik Jan, Walter A. Kosters, Jan N. van Rijn, and Jonathan K. Vis. "Acyclic Constraint Logic and Games." ICGA Journal 37, no. 1 (2014): 3–16. http://dx.doi.org/10.3233/icg-2014-37102.

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29

Kakas, A. C., A. Michael, and C. Mourlas. "ACLP: Abductive Constraint Logic Programming." Journal of Logic Programming 44, no. 1-3 (2000): 129–77. http://dx.doi.org/10.1016/s0743-1066(99)00075-8.

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30

Dovier, Agostino, Carla Piazza, Enrico Pontelli, and Gianfranco Rossi. "Sets and constraint logic programming." ACM Transactions on Programming Languages and Systems 22, no. 5 (2000): 861–931. http://dx.doi.org/10.1145/365151.365169.

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31

Cárdenas-Viedma, M. A., and R. Marín. "FTCLogic: Fuzzy Temporal Constraint Logic." Fuzzy Sets and Systems 363 (May 2019): 84–112. http://dx.doi.org/10.1016/j.fss.2018.05.014.

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32

Escobar, Santiago, and Moreno Falaschi. "Functional and (Constraint) Logic Programming." Information and Computation 235 (April 2014): 1–2. http://dx.doi.org/10.1016/j.ic.2014.01.007.

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33

Hooker, John N. "Logic, Optimization, and Constraint Programming." INFORMS Journal on Computing 14, no. 4 (2002): 295–321. http://dx.doi.org/10.1287/ijoc.14.4.295.2828.

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34

Bistarelli, Stefano, Ugo Montanari, and Francesca Rossi. "Semiring-based constraint logic programming." ACM Transactions on Programming Languages and Systems 23, no. 1 (2001): 1–29. http://dx.doi.org/10.1145/383721.383725.

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35

Jaffar, Joxan, and Michael J. Maher. "Constraint logic programming: a survey." Journal of Logic Programming 19-20 (May 1994): 503–81. http://dx.doi.org/10.1016/0743-1066(94)90033-7.

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36

Mackworth, Alan K. "The logic of constraint satisfaction." Artificial Intelligence 58, no. 1-3 (1992): 3–20. http://dx.doi.org/10.1016/0004-3702(92)90003-g.

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37

Stuckey, P. J. "Negation and Constraint Logic Programming." Information and Computation 118, no. 1 (1995): 12–33. http://dx.doi.org/10.1006/inco.1995.1048.

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38

López-Fraguas, F. Javier, Mario Rodríguez-Artalejo, and Rafael del Vado Vírseda. "Constraint Functional Logic Programming Revisited." Electronic Notes in Theoretical Computer Science 117 (January 2005): 5–50. http://dx.doi.org/10.1016/j.entcs.2004.06.030.

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39

FRÜHWIRTH, THOM. "Temporal Annotated Constraint Logic Programming." Journal of Symbolic Computation 22, no. 5-6 (1996): 555–83. http://dx.doi.org/10.1006/jsco.1996.0066.

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40

Ligęza, Antoni. "Models and Tools for Improving Efficiency in Constraint Logic Programming." Decision Making in Manufacturing and Services 5, no. 1 (2011): 69–78. http://dx.doi.org/10.7494/dmms.2011.5.1.69.

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Анотація:

Constraint Satisfaction Problems typically exhibit strong combinatorial explosion. In this paper we present some models and techniques aimed at improving efficiency in Constraint Logic Programming. A hypergraph model of constraints is presented and an outline of strategy planning approach focused on entropy minimization is put forward. An example cryptoaritmetic problem is explored in order to explain the proposed approach.

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41

Chen, X., H. Hsieh, F. Balarin, and Y. Watanabe. "Logic of Constraints: A Quantitative Performance and Functional Constraint Formalism." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 23, no. 8 (2004): 1243–55. http://dx.doi.org/10.1109/tcad.2004.831575.

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42

MAHER, MICHAEL J. "Contractibility for open global constraints." Theory and Practice of Logic Programming 17, no. 4 (2017): 365–407. http://dx.doi.org/10.1017/s1471068417000126.

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Анотація:

AbstractOpen forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems, where problem construction and problem solving are interleaved, and fit naturally within constraint logic programming. However, in general, filtering that is sound for a global constraint can be unsound when the constraint is open. This paper provides a simple characterization, called contractibility, of the constraints, where filtering remains sound when the constraint is open. With this c

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43

LEACH, JAVIER, SUSANA NIEVA, and MARIO RODRÍGUEZ-ARTALEJO. "Constraint Logic Programming with Hereditary Harrop formulas." Theory and Practice of Logic Programming 1, no. 4 (2001): 409–45. http://dx.doi.org/10.1017/s1471068401001041.

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Анотація:

Constraint Logic Programming (CLP) and Hereditary Harrop formulas (HH) are two well known ways to enhance the expressivity of Horn clauses. In this paper, we present a novel combination of these two approaches. We show how to enrich the syntax and proof theory of HH with the help of a given constraint system, in such a way that the key property of HH as a logic programming language (namely, the existence of uniform proofs) is preserved. We also present a procedure for goal solving, showing its soundness and completeness for computing answer constraints. As a consequence of this result, we obta

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44

PIMENTEL, ELAINE, CARLOS OLARTE, and VIVEK NIGAM. "A Proof Theoretic Study of Soft Concurrent Constraint Programming." Theory and Practice of Logic Programming 14, no. 4-5 (2014): 649–63. http://dx.doi.org/10.1017/s147106841400026x.

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AbstractConcurrent Constraint Programming (CCP) is a simple and powerful model for concurrency where agents interact by telling and asking constraints. Since their inception, CCP-languages have been designed for having a strong connection to logic. In fact, the underlying constraint system can be built from a suitable fragment of intuitionistic (linear) logic -ILL- and processes can be interpreted as formulas in ILL. Constraints as ILL formulas fail to represent accurately situations where “preferences” (called soft constraints) such as probabilities, uncertainty or fuzziness are present. In o

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45

Craig, I. "Constraint Logic Programming: Selected Research & Constraint-Based Reasoning." Computer Journal 37, no. 6 (1994): 557–59. http://dx.doi.org/10.1093/comjnl/37.6.557-a.

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46

SHEN, YI-DONG, JIA-HUAI YOU, and LI-YAN YUAN. "Characterizations of stable model semantics for logic programs with arbitrary constraint atoms." Theory and Practice of Logic Programming 9, no. 4 (2009): 529–64. http://dx.doi.org/10.1017/s1471068409990056.

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AbstractThis paper studies the stable model semantics of logic programs with (abstract) constraint atoms and their properties. We introduce a succinct abstract representation of these constraint atoms in which a constraint atom is represented compactly. We show two applications. First, under this representation of constraint atoms, we generalize the Gelfond–Lifschitz transformation and apply it to define stable models (also called answer sets) for logic programs with arbitrary constraint atoms. The resulting semantics turns out to coincide with the one defined by Son et al. (2007), which is ba

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47

ZHANG, YUANLIN, and ROLAND H. C. YAP. "Solving functional constraints by variable substitution." Theory and Practice of Logic Programming 11, no. 2-3 (2011): 297–322. http://dx.doi.org/10.1017/s1471068410000591.

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AbstractFunctional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ Constraint Satisfaction Problem(s)-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other nonfunctional constraints. It also sol

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Patel, Ambresh, and Ritesh Sadiwala. "Performance Analysis of Various Complementary Metaloxide Semiconductor Logics for High Speed Very Large Scale Integration Circuits." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 15, no. 01 (2023): 91–95. http://dx.doi.org/10.18090/10.18090/samriddhi.v15i01.13.

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The demand for VLSI low voltage high-performance low power systems are increasing significantly. Today's deviceapplications necessitate a system that consumes little power and conserves performance. Recent battery-powered lowvoltagedevices optimize power and high-speed constraints. Aside from that, there is a design constraint with burst-modetype integrated circuits for small devices to scale down. Low voltage low power static CMOS logic integrated circuitsoperate at a slower rate and cannot be used in high performance circuits. As a result, dynamic CMOS logic is used inintegrated circuits bec

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SCHRIJVERS, TOM, BART DEMOEN, and DAVID S. WARREN. "TCHR: a framework for tabled CLP." Theory and Practice of Logic Programming 8, no. 04 (2008): 491–526. http://dx.doi.org/10.1017/s147106840800327x.

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AbstractTabled Constraint Logic Programming is a powerful execution mechanism for dealing with Constraint Logic Programming without worrying about fixpoint computation. Various applications, e.g. in the fields of program analysis and model checking, have been proposed. Unfortunately, a high-level system for developing new applications is lacking, and programmers are forced to resort to complicated ad hoc solutions.This papers presents TCHR, a high-level framework for tabled Constraint Logic Programming. It integrates in a light-weight manner Constraint Handling Rules (CHR), a high-level langua

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Mizoguchi, Fumio, and Hayato Ohwada. "Constrained relative least general generalization for Inducing Constraint Logic Programs." New Generation Computing 13, no. 3-4 (1995): 335–68. http://dx.doi.org/10.1007/bf03037230.

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