Analysis and Visualization Tools for Constraint Programming [electronic resource] :Constraint Debugging /
Contributor(s): Deransart, Pierre [editor.] | Hermenegildo, Manuel V [editor.] | Małuszynski, Jan [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 1870Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.Description: XXII, 370 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540400165.Subject(s): Computer science | Computer programming | Software engineering | Programming languages (Electronic computers) | Mathematical logic | Artificial intelligence | Computer Science | Programming Languages, Compilers, Interpreters | Programming Techniques | Software Engineering | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal LanguagesOnline resources: Click here to access online
Debugging of Constraint Programs: The DiSCiPl Methodology and Tools -- Debugging of Constraint Programs: The DiSCiPl Methodology and Tools -- I. Correctness Debugging -- An Assertion Language for Constraint Logic Programs -- A Generic Preprocessor for Program Validation and Debugging -- Assertions with Constraints for CLP Debugging -- Locating Type Errors in Untyped CLP Programs -- Declarative Diagnosis in the CLP Scheme -- II. Performance Debugging -- Visual Tools to Debug Prolog IV Programs -- Search-Tree Visualisation -- Towards a Language for CLP Choice-Tree Visualisation -- Tools for Search-Tree Visualisation: The APT Tool -- Tools for Constraint Visualisation: The VIFID/TRIFID Tool -- Debugging Constraint Programs by Store Inspection -- Complex Constraint Abstraction: Global Constraint Visualisation -- III. Test Cases -- Using Constraint Visualisation Tools.
Coordinating production across a supply chain, designing a new VLSI chip, allocating classrooms or scheduling maintenance crews at an airport are just a few examples of complex (combinatorial) problems that can be modeled as a set of decision variables whose values are subject to a set of constraints. The decision variables may be the time when production of a particular lot will start or the plane that a maintenance crew will be working on at a given time. Constraints may range from the number of students you can ?t in a given classroom to the time it takes to transfer a lot from one plant to another.Despiteadvancesincomputingpower,manyformsoftheseandother combinatorial problems have continued to defy conventional programming approaches. Constraint Logic Programming (CLP) ?rst emerged in the mid-eighties as a programming technique with the potential of signi?cantly reducing the time it takes to develop practical solutions to many of these problems, by combining the expressiveness of languages such as Prolog with the compu- tional power of constrained search. While the roots of CLP can be traced to Monash University in Australia, it is without any doubt in Europe that this new software technology has gained the most prominence, bene?ting, among other things, from sustained funding from both industry and public R&D programs over the past dozen years. These investments have already paid o?, resulting in a number of popular commercial solutions as well as the creation of several successful European startups.