Operational semantics of constraint logic programming over finite domains

Author(s):  
Pascal Hentenryck ◽  
Yves Deville
2007 ◽  
Vol 7 (5) ◽  
pp. 537-582 ◽  
Author(s):  
ANTONIO J. FERNÁNDEZ ◽  
TERESA HORTALÁ-GONZÁLEZ ◽  
FERNANDO SÁENZ-PÉREZ ◽  
RAFAEL DEL VADO-VÍRSEDA

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 constraints. We describe the syntax of the language, its type discipline, and its declarative and operational semantics. We also describe\toy(fd)$, an implementation forCFLP($\fd$), and a comparison of our approach with respect toCLP($\fd$) from a programming point of view, showing the new features we introduce. And, finally, we show a performance analysis which demonstrates that our implementation is competitive with respect to existingCLP($\fd$) systems and that clearly outperforms the closer approach toCFLP($\fd$).


2018 ◽  
Vol 161 (1-2) ◽  
pp. 9-27 ◽  
Author(s):  
Federico Bergenti ◽  
Stefania Monica ◽  
Gianfranco Rossi

1995 ◽  
Vol 04 (01n02) ◽  
pp. 3-32 ◽  
Author(s):  
J.H.M. LEE ◽  
V.W.L. TAM

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 and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.


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