Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.

Experience using constraint programming to solve real-life problems has shown that finding an efficient solution to the problem often requires experimentation with different constraint solvers or even building a problem-specific constraint solver. HAL is a new constraint logic programming language expressly designed to facilitate this process. It provides a well-defined solver interface, mutable global variables for implementing a constraint store, and dynamic scheduling which support combining, extending and writing new constraint solvers. Equally importantly, HAL supports semi-optional type, mode and determinacy declarations. These allow natural constraint specification by means of type overloading, better compile-time error checking and generation of more efficient run-time code.

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the efficiency of model-based diagnosis, we propose a model representation at several levels of detail, and define three refinement (abstraction) operators. We specify formal conditions that have to be satisfied by the hierarchical representation, and emphasize that the multi-level scheme is independent of any particular single-level model representation. The hierarchical diagnostic algorithm which we define turns out to be very general. We show that it emulates the bisection method, and can be used for hierarchical constraint satisfaction. We apply the hierarchical modeling principle and diagnostic algorithm to a medium-scale medical problem. The performance of a four-level qualitative model of the heart is compared t...

Proposed in this paper is a novel data model and its language for querying object-oriented databases where objects may hold spatial, temporal or constraint data, conceptually represented by linear equality and inequality constraints. The proposed LyriC language is designed to provide a uniform and flexible framework for diverse application realms such as (1) constraint-based design in two-, three-, or higher-dimensional space, (2) large-scale optimization and analysis, based mostly on linear programming techniques, and (3) spatial and geographic databases. LyriC extends flat constraint query languages, especially those for linear constraint databases, to structurally complex objects. The extension is based on the object-oriented paradigm, where constraints are treated as first-class objects that are organized in classes. The query language is an extension of the language XSQL, and is built around the idea of extended path expressions. Path expressions in a query traverse nested struct...

In recent years, it has been increasingly recognized that constraint and integer programming have orthogonal and complementary strengths in stating and solving combinatorial optimization applications. In addition, their integration has become an active research topic. The optimization programming language opl was a first attempt at integrating these technologies both at the language and at the solver levels. In particular, opl is a modeling language integrating the rich language of constraint programming and the ability to specify search procedures at a high level of abstraction. Its implementation includes both constraint and mathematical programming solvers, as well as some cooperation schemes to make them collaborate on a given problem. The purpose of this paper is to illustrate, using opl, the constraint-programming approach to combinatorial optimization and the complementary strengths of constraint and integer programming. (Artificial Intelligence; Computer Science; Integer Programming) 1.

In this paper we study unfold/fold transformations for constraint logic programs (CLP) with dynamic scheduling and for concurrent constraint programming (ccp). We define suitable applicability conditions for this transformations which ensure us that the original and the transformed program have the same results of successful computations and have the same deadlocked derivations. The possible applications of these results are twofold. On one hand we can use the unfold/fold system to optimize CLP and ccp programs while preserving their intended meaning and in particular without the risk of introducing deadlocks. On the other hand, unfold/fold transformations can be used for proving deadlock freedom for a class of queries in a given program: to this aim it is sufficient to specialize the program with respect to the given queries in such a way that the resulting program is trivially deadlock free. As shown by several interesting examples, this yields a methodology for proving deadlock free...

. We report on a new way of handling non-linear arithmetic constraints and its implementation into the QUAD-CLP(R) language. Important properties of the problem at hand are a discretization through geometric equivalence classes and decomposition into convex pieces. A case analysis of those equivalence classes leads to a relaxation (and sometimes recasting) of the original constraints into linear constraints, much easier to handle. Complementing earlier expositions in [18] and [19], the present focus is on applications upholding its worth. 1. Motivation. This paper presents the constraint programming language QUAD-CLP(R) which offers a powerful novel solving strategy for non-linear arithmetic constraints under the computing paradigm of logic programming. Emphasis will be given here to the techniques involved in the constraint solver for quadratic constraints over R and to applications making use of this added power. Despite the enormous potential of non-linear arithmetic constraints in...

this document; the book by Sterling and Shapiro [20] can serve as a suitable introductory text. Further technical information on CLP(R) is available on language design and implementation [12, 13], metaprogramming [7] and delay mechanisms [14]. Additionally, much has been written about applications in electrical engineering [6, 18], differential equations [5, 8], temporal reasoning [1, 2, 3], protocol testing [4], structural analysis and synthesis [15], mechanical engineering [21], user interfaces [23], model-based diagnosis [24], options trading [16], music theory [9], molecular biology [22], etc.

Power-aware systems are those that must exploit a wide range of power/performance tradeoffs in order to adapt to the power availability and application requirements. They require the integration of many novel power management techniques, ranging from voltage scaling to subsystem shutdown. However, those techniques do not always compose synergistically with each other; in fact, they can combine subtractively and often yield counterintuitive, and sometimes incorrect, results in the context of a complete system. This can become a serious problem as more of these power aware systems are being deployed in mission critical applications.

Constraints are a powerful general paradigm for representing knowledge in intelligent systems. The standard constraint satisfaction paradigm involves variables over a discrete value domain and constraints which restrict the solutions to allowed value combinations. This standard paradigm is inapplicable to problems which are either: (a) mixed, involving both numeric and discrete variables, or (b) conditional? containing variables whose existence depends on the values chosen for other variables, or (c) both, conditional and mixed.