Constraint Satisfaction with an Object-Oriented Knowledge Representation Language

This paper gives a detailed presentation of constraint satisfaction in the hybrid LAURE language. LAURE is an object-oriented language for Artificial Intelligence (AI) applications which allows the user to combine rules, constraints and methods that cooperate on the same objects in the same program. We illustrate why this extensibility is necessary to solve some large and difficult problems by presenting a real-life application of LAURE. We describe the syntax and the various modes in which constraints may be used, as well as the tools that are proposed by LAURE to extend constraint resolution. The resolution strategy as well as some implementation details are given to explain how we obtain good performances

Constraints in an Object-Oriented Deductive Database

This paper relates our experience in integrating constraint resolution into an object-oriented deductive system. We motivate this work by showing that disjunctive information and global constraints fit naturally in an object-oriented model and are actually necessary to perform common tasks. We identify three difficulties in developing such an extended object-oriented system (namely, compilation, domain reduction, and heuristics) and propose a solution for each. We provide a formal semantics and prove the correctness of those three techniques. We illustrate the performance results with the implementation we have built in LAURE. 1. Introduction Non-traditional database applications, such as design and planning (scheduling/ resource assignment), try to use object-oriented databases, because the complex structures (objects) that arise naturally when describing the design of a telephone switch or when assigning tasks to a pool of technicians fit the object-oriented paradigm more naturally..

Etude et realisation d'un langage objet : LORE

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain

This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy search and the introduction of randomization and de-randomization within our combination algebra. This algebra is made of terms that represent optimization algorithms, following the approach of SALSA [1], which can be generated or tuned automatically using a learning meta-strategy [2]. In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search

Jobshop Scheduling with task intervals

This paper presents a technique for scheduling based on sets of tasks. Used within a branch & bound algorithm, it cuts branches very efficiently. It is thus well adapted for proofs of optimality. This technique solves typical hard 10 \Theta 10 problems within a few thousand backtracks and is also adapted to large problems. LA21, a 15 \Theta 10 problem open since 1984 is solved and a new bound is given for LA29, a 20 \Theta 10 problem. keywords: jobshop, scheduling, branch and bound, edge finding Scheduling An n \Theta m jobshop scheduling problem is composed of a set of jobs J = j 1 ; :::; j n to be performed on a set of machines M = r 1 ; :::; r m . A job j i = j i 1 ; :::; j i m is an ordered sequence of tasks to which a machine use(j i k ) and a duration d(j i k ) are associated. A schedule is a set of positive starting times time(t) for the tasks t such that ffl for all i 2 f1; ::; ng; k 2 f1; ::; m \Gamma 1g; time(j i k+1 ) time(j i k ) + d(j i k ) ffl use(t) = us..

Solving small TSPs with constraints

This paper presents a set of techniques that makes constraint programming a technique of choice for solving small (up to 30 nodes) traveling salesman problems. These techniques include a propagation scheme to avoid intermediate cycles (a global constraint), a branching scheme and a redundant constraint that can be used as a bounding method. The resulting improvement is that we can solve problems twice larger than those solved previously with constraint programming tools. We evaluate the use of Lagrangean Relaxation to narrow the gap between constraint programming and other Operations Research techniques and we show that improved constraint propagation has now a place in the array of techniques that should be used to solve a traveling salesman problem

Solving Various Weighted Matching Problems with Constraints

This paper studies the resolution of (augmented) weighted matching problems within a constraint programming framework. The first contribution of the paper is a set of branch-and-bound techniques that improves substantially the performance of algorithms based on constraint propagation and the second contribution is the introduction of weighted matching as a global constraint (MinWeightAllDifferent), that can be propagated using specialized incremental algorithms from Operations Research. We first compare programming techniques that use constraint propagation with specialized algorithms from Operations Research, such as the Busaker and Gowen flow algorithm or the Hungarian method. Although CLP is shown not to be competitive with specialized polynomial algorithms for "pure" matching problems, the situation is different as soon as the problems are modified with additional constraints. Using the previously mentioned set of techniques, a simpler branch-andbound algorithm based on constraint ..