this paper we argue that many user interface construction problems are handled more naturally and elegantly by multi-way constraints than by one-way constraints. We present pseudocode for an incremental multi-way constraint satisfaction algorithm, DeltaBlue, and describe experience in using the algorithm in two user interface toolkits. Finally, we provide performance figures demonstrating that multi-way constraint solvers can be entirely competitive in performance with one-way constraint solvers

Is there one logical definition of diagnosis? In this paper I argue that the answer to this question is "no". This paper is about the pragmatics of using logic for diagnosis; we show how two popular proposals for using logic for diagnosis, (namely abductive and consistency-based approaches) can be used to solve diagnostic tasks. The cases with only knowledge about how normal components work (any deviation being an error) and where there are fault models (we try to find a covering of the observations) are considered as well as the continuum between. The result is that there are two fundamentally different, but equally powerful diagnostic paradigms. They require different knowledge about the world, and different ways to think about a domain. This result indicates that there may not be an axiomatisation of a domain that is independent of how the knowledge is to be used.

Constraint logic programming (CLP) is a new class of declarative programming lan-guages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solv-ing a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: test-pattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools.

A constraint describes a relation to be maintained

This report introduces a classification scheme for the global constraints. This classification is based on four basic ingredients from which one can generate almost all existing global constraints and come up with new interesting constraints. Global constraints are defined in a very concise way, in term of graph properties that have to hold, where the graph is a structured network of same elementary constraints. Since this classification is based on the internal structure of the global constraints it is also a strong hint for the pruning algorithms of the global constraints. Keywords Constraint, finite domain, global constraint, classification, resource constraint scheduling, graph partitioning, timetabling. 2 Table of contents Table of contents ....................................................................................................................................................... 2 Table of figures.........................................................................

Constraint logic programming languages such asCHIP[26,5] havedemonstrated the practicality ofdeclarative languages supporting consistency techniques and nondeterminism. Nevertheless they su er from the black-box e ect: the programmer must work with a monolithic, unmodi able, inextensible constraint-solver. This problem can be overcome within the logically and computationally richer concurrent constraint (cc) programming paradigm [17]. We showthat some basic constraint-operations currently hardwired into constraint-solvers can be abstracted and made available as combinators in the programming language. This allows complex constraint-solvers to be decomposed into logically clean and e ciently implementable cc programs over a much simpler constraint system. In particular, we show that the CHIP constraint-solver can be simply programmed in cc(FD), acc language with an extremely simple built-in constraint solver for nite domains. 1

This paper describes our experience with a simple modeling and programming approach for increasing the amount of constraint propagation in the constraint solving process. The idea, although similar to redundant constraints, is based on the concept of redundant modeling. We introduce the notions of CSP model and model redundancy, and show how mutually redundant models can be combined and connected using channeling constraints. The combined model contains the mutually redundant models as sub-models. Channeling constraints allow the sub-models to cooperate during constraint solving by propagating constraints freely amongst the sub-models. This extra level of pruning and propagation activities becomes the source of execution speedup. We perform two case studies to evaluate the effectiveness and efficiency of our method. The first case study is based on the simple and well-known n-queens problem, while the second case study applies our method in the design and construction of a real-life ...

The goal of this paper is to develop models and methods that use complementary strengths of Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques to solve problems that are otherwise intractable if solved using either of the two methods. The class of problems considered in this paper have the characteristic that only a subset of the binary variables have non-zero objective function coefficients if modeled as an MILP. This class of problems is formulated as a hybrid MILP/CP model that involves some of the MILP constraints, a reduced set of the CP constraints, and equivalence relations between the MILP and the CP variables. An MILP/CP based decomposition method and an LP/CP-based branch-and-bound algorithm are proposed to solve these hybrid models. Both these algorithms rely on the same relaxed MILP and feasibility CP problems. An application example is considered in which the least-cost schedule has to be derived for processing a set of orders with release and due dates using a set of dissimilar parallel machines. It is shown that this problem can be modeled as an MILP, a CP, a combined MILP-CP OPL model (Van Hentenryck 1999), and a hybrid MILP/CP model. The computational performance of these models for several sets shows that the hybrid MILP/CP model can achieve two to three orders of magnitude reduction in CPU time.

This report surveys the major developments in sequential Prolog implementation during the period 1983--1993. In this decade, implementation technology has matured to such a degree that Prolog has left the university and become useful in industry. The survey is divided into four parts. The first part gives an overview of the important technical developments starting with the Warren Abstract Machine (WAM). The second part presents the history and the contributions of the major software and hardware systems. The third part charts the evolution of Prolog performance since Warren's DEC-10 compiler. The fourth part extrapolates current trends regarding the evolution of sequential logic languages, their implementation, and their role in the marketplace.

This paper describes the Oz Explorer and its implementation. The Explorer is a visual constraint programming tool intended to support the development of constraint programs. It uses the search tree of a constraint problem as its central metaphor. Exploration and visualization of the search tree are userdriven and interactive. The constraints of any node in the tree are available first-class: predefined or user-defined procedures can be used to display or analyze them. The Explorer is a fast and memory efficient tool intended for the development of real-world constraint programs. The Explorer is implemented in Oz using first-class computation spaces. There is no fixed search strategy in Oz. Instead, first-class computation spaces allow to program search engines. The Explorer is one particular example of a user-guided search engine. The use of recomputation to trade space for time makes it possible to solve large real-world problems, which would use too much memory otherwise.