Abstract: Prolog can be extended by a system of constraints on closed intervals to perform declarative relational arithmetic. Imposing constraints on an interval can narrow its range and propagate the narrowing to other intervals related to it by constraint equations or inequalities. Relational interval arithmetic can be used to contain oating point errors and, when combined with Prolog backtracking, to obtain numeric solutions to linear and non-linear rational constraint satisfaction problems over the reals (e.g. n-degree polynomial equations). This technique di ers from other constraint logic programming (CLP) systems like CLP(<) or Prolog-III in that it does not do any symbolic processing.

Abstract: Chcl is a Connectionist inference system for Horn logic which is based on the Connection method and uses Limited resources. This paper gives an overview of the system and its implementation.

Abstract: Conventional methods for the parametric design of engineering structures rely on the iterative re-use of analysis programs in order to converge on a satisfactory solution. Since finite element and other analysis programs require considerable computer resources, this research proposes a general method to minimize their use, by utilizing constraint-based reasoning to carry out redesign. A problem-solver, consisting of constraint networks which express basic relationships between individual design parameters and variables, is attached to the analysis programs. Once an initial design description has been set out using the conventional analysis programs, the networks can then reason about required adjustments in order to find a consistent set of parameter values. We describe how global constraints representing standard design behavioral equations are decomposed to form binary constraint networks. The networks use approximate reasoning to determine dependencies between key parameters, and after an adjustment has been made, use exact relationship information to update only those parts of the design description that are affected by the adjustment. We illustrate the ideas by taking as an example the design of a continuous prestressed concrete beam.

Abstract: A method is introduced which adds a padding term to each constraint that is proportional to the gradient of the constraint. One way to implement this approach is to determine the gradients of the constraints at the unpadded optimum design, to modify the constraint allowables, and then to restart the optimization. A second implementation is proposed which imbeds the padding calculation into the optimization process by updating the padding term periodically during the optimization process. Finally an implementation which uses nonlinear constraint approximations and a second order update method is described

Abstract: We address the problem of building intelligent query systems to reason about linear arithmetic constraints. The central issue is the development of tools for testing solvability, for constraints representation, for incremental updates and for intelligent feedback. The concept of parametric queries introduced in the context of constraint logic programming provides the starting point for this study. The relevance of this approach is illustrated by examples from the domain of spatial reasoning.

Abstract: This paper explores the interrelationships between methods developed in mathematical programming to discover the structure of constraint (feasibility) sets and constraint propagation over networks used by some AI systems to perform inferences about quantities. It is shown that some constraint set problems in mathematical programming are equivalent to inferencing problems for constraint networks with interval labels. This makes the inference and query capabilities associated with AI systems that use logic programming, directly accessible to mathematical programming systems. On the other hand, traditional and newer methods which mathematical programming uses to obtain information about its associated feasibility set can be used to determine the propagation of constraints in a network of nodes of an AI system. When viewed from this point of view, AI problems can access additional mathematical programming analytical tools including new ways to incorporate qualitative data into constraint sets via interval and fuzzy arithmetic.

Abstract: In the past few years, an extensive amount of empirical evidence has proved the practical value of finite-domain constraint logic programming (CLP) Using special CLP-systems, many constraint satisfaction applications have been programmed very quickly and the resulting programs have a good performance In this paper, we show how to implement a finite-domain CLP on top of a PROLOG-system equipped with a delay mechanism The advantages are that the language features are easy to implement, the overhead caused both to the underlying PROLOG-system and to the CLP-environment itself are small and that the system is relatively portable

Abstract: A bottom-up generation algorithm for principle-based grammars is proposed. Bottom-up generation has (1) an inefficiency because of a high degree of nondeterminism, (2) a limitation caused by inability to process logical forms produced by grammar rules, and (3) an identity semantic problem. This paper describes a solution to these problems and implementation issues for the algorithm using a constraint logic programming language.

Abstract: A constraint-based system for automating the acquisition of problem-solving knowledge is described. The approach is novel in attempting to compile rules from the observation of constraint-based, relaxation-based problem solving. The system has three main components; a constraint-based problem solver, a rule-compiler and a rule-base problem solver. A relation consistency algorithm is the backbone of the constraint-based problem solver. One advantage of this method is that customized expert systems can be built by manipulating the problems used for learning. Experiments were performed to evaluate a prototype learning system and some extensions. >

Abstract: This paper investigates the maximum entropy restoration of blurred binary image.In concerning with the binary property of image,a new maximum entropy restoration methodwith binary constraint is proposed.The properties of existence and uniqueness of solution arediscussed.The problem of maximum of entropy with two constraints is solved and the corre-sponding algorithm is given.In this paper,the maximum bounded entropy principle is employedconcerning the prior knowledge of binary image,and the maximum bounded entropy restora-tion method with binary constraint is put forward.The proposes methods,Wiener filter(WF)restoration method and maximum entropy restoration method are compared.The experimen-tal results show that the maximum entropy restoration method and maximum bounded entropyrestoration method with binary constraint can improve the quality of restored image.

Abstract: The need to respond quickly has a bearing on the kinds of computing mechanism that can be considered. Even where speed is not vital to the application, it is likely that the kind of mechanism discussed here will correspond most closely to the biological solution, and the recent stagnation of AI development inspires respect for the latter.

Abstract: Constraint Logic Programming (CLP) tries to unify the best from Logic Programming and Constraint Satisfaction. However, implementors of languages of the CLP class such as the CLP (R) must solve some unique problems such as constraint backtracking and devising an incremental constraint solver. This paper describes how the Simplex algorithm was adapted to serve as a constraint solver in a prototype CLP (R) system. The algorithm can handle equations as well as inequalities. The Simplex algorithm can be incrementalized easily by dividing it into invariant preserving steps. Constraint backtracking can be implemented efficiently by changing slack variable types.

Abstract: An incremental constraint solver, the DeltaBlue algorithm maintains an evolving solution to the constraint hierarchy as constraints are added and removed DeltaBlue minimizes the cost of finding a new solution after each change by exploiting its knowledge of the last solution