Over-Constrained Systems

by Michael Jampel (Editor), Eugene Freuder (Editor), Michael Maher (Editor)

Overview

This volume presents a collection of refereed papers reflecting the state of the art in the area of over-constrained systems.

Besides 11 revised full papers, selected from the 24 submissions to the OCS workshop held in conjunction with the First International Conference on Principles and Practice of Constraint Programming, CP '95, held in Marseilles in September 1995, the book includes three comprehensive background papers of central importance for the workshop papers and the whole field. Also included is an introduction by one of the volume editors together with a bibliography listing 243 entries. All in all this is a very useful reference book relevant for all researchers and practitioners interested in hierarchical, partial, and over-constrained systems.

Product Details

ISBN-13:	9783540614791
Publisher:	Springer Berlin Heidelberg
Publication date:	08/16/1996
Series:	Lecture Notes in Computer Science , #1106
Edition description:	1996
Pages:	314
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

A brief overview of over-constrained systems.- Constraint hierarchies.- Partial constraint satisfaction.- Semiring-based CSPs and valued CSPs: Basic properties and comparison.- Defeasible constraint solving.- Transforming ordered constraint hierarchies into ordinary constraint systems.- A compositional theory of constraint hierarchies (Operational semantics).- Heuristic methods for over-constrained constraint satisfaction problems.- Cascaded directed arc consistency and no-good learning for the maximal constraint satisfaction problem.- Partial arc consistency.- Dynamic constraint satisfaction with conflict management in design.- Specifying over-constrained problems in default logic.- Implementing constraint relaxation over finite domains using assumption-based truth maintenance systems.- Experiences in solving constraint relaxation networks with Boltzmann Machines.- Solving over-constrained CSP using weighted OBDDs.