Constraint programming

About: Constraint programming is a research topic. Over the lifetime, 8175 publications have been published within this topic receiving 190683 citations.

Using dynamic programming for solving variational problems in vision

Abstract: Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed. >

Prolog Programming for Artificial Intelligence

Abstract: From the Publisher: B> This best-selling guide to Prolog has been fully revised and extended to provide an even greater range of applications, enhancing its value as a stand-alone guide to Prolog, artificial intelligence, or AI programming. Ivan Bratko discusses natural language processing with grammar rules, planning, and machine learning. The coverage of meta-programming includes meta-interpreters and object-oriented programming in Prolog. The new edition includes coverage of: constraint logic programming; qualitative reasoning; inductive logic programming; recently developed algorithms; belief networks for handling uncertainty; and a major update on machine learning. This book is aimed at programmers who need to learn AI programming.

Logic programs with stable model semantics as a constraint programming paradigm

Abstract: Logic programming with the stable model semantics is put forward as a novel constraint programming paradigm. This paradigm is interesting because it bring advantages of logic programming based knowledge representation techniques to constraint programming and because implementation methods for the stable model semantics for ground (variabledfree) programs have advanced significantly in recent years. For a program with variables these methods need a grounding procedure for generating a variabledfree program. As a practical approach to handling the grounding problem a subclass of logic programs, domain restricted programs, is proposed. This subclass enables efficient grounding procedures and serves as a basis for integrating builtdin predicates and functions often needed in applications. It is shown that the novel paradigm embeds classical logical satisfiability and standard (finite domain) constraint satisfaction problems but seems to provide a more expressive framework from a knowledge representation point of view. The first steps towards a programming methodology for the new paradigm are taken by presenting solutions to standard constraint satisfaction problems, combinatorial graph problems and planning problems. An efficient implementation of the paradigm based on domain restricted programs has been developed. This is an extension of a previous implementation of the stable model semantics, the Smodels system, and is publicly available. It contains, e.g., builtdin integer arithmetic integrated to stable model computation. The implementation is described briefly and some test results illustrating the current level of performance are reported.

Programming with constraints: an introduction

Abstract: Part 1 Constraints: constraints simplifications, optimization and implication finite constraint domains. Part 2 Constraint logic programming: constraint logic programming simple modelling using data structures controlling search modelling with finite domain constraints advanced programming techniques CLP systems. Part 3 Other constraint programming languages: constraint databases other constraint programming languages.

Empirical Studies of Programming Knowledge

Abstract: We suggest that expert programmers have and use two types of programming knowledge: 1) programming plans, which are generic program fragments that represent stereotypic action sequences in programming, and 2) rules of programming discourse, which capture the conventions in programming and govern the composition of the plans into programs. We report here on two empirical studies that attempt to evaluate the above hypothesis. Results from these studies do in fact support our claim.