Constraint programming in computational linguistics

Constraint programming is a programming paradigm that was originally invented in computer science to deal with hard combinatorial problems. Recently, constraint programming has evolved into a technology which permits to solve hard industrial scheduling and optimization problems. We argue that existing constraint programming technology can be useful for applications in natural language processing. Some problems whose treatment with traditional methods requires great care to avoid combinatorial explosion of (potential) readings seem to be solvable in an efficient and elegant manner using constraint programming. We illustrate our claim by two recent examples, one from the area of underspecified semantics and one from parsing

CP debugging needs and tools

Conventional programming techniques are not well suited for solving many highly combinatorial industrial problems, like scheduling, decision making, resource allocation or planning. Constraint Programming (CP), an emerging software technology, offers an original approach allowing for efficient and flexible solving of complex problems, through combined implementation of various constraint solvers and expert heuristics. Its applications are increasingly elded in various industries

Constraint Satisfaction Techniques for Combinatorial Problems

The last two decades have seen extraordinary advances in tools and techniques for constraint satisfaction. These advances have in turn created great interest in their industrial applications. As a result, tools and techniques are often tailored to meet the needs of industrial applications out of the box. We claim that in the case of abstract combinatorial problems in discrete mathematics, the standard tools and techniques require special considerations in order to be applied effectively. The main objective of this thesis is to help researchers in discrete mathematics weave through the landscape of constraint satisfaction techniques in order to pick the right tool for the job. We consider constraint satisfaction paradigms like satisfiability of Boolean formulas and answer set programming, and techniques like symmetry breaking. Our contributions range from theoretical results to practical issues regarding tool applications to combinatorial problems. We prove search-versus-decision complexity results for problems about backbones and backdoors of Boolean formulas. We consider applications of constraint satisfaction techniques to problems in graph arrowing (specifically in Ramsey and Folkman theory) and computational social choice. Our contributions show how applying constraint satisfaction techniques to abstract combinatorial problems poses additional challenges. We show how these challenges can be addressed. Additionally, we consider the issue of trusting the results of applying constraint satisfaction techniques to combinatorial problems by relying on verified computations

Constraint-based adaptation for complex space configuration in building services

In this paper an object-based CAD programming is used to take advantage of standardization to handle the schematic design, sizing and layout planning for ceiling mounted fan coil system in a building ceiling void. In order to deal with more complex geometry and real building size, we have used a hybrid approach combining case-based reasoning and constraint programming techniques. Very often, building services engineers use previous solutions and adapt them to new problems. Case-based reasoning mirrors this practical approach and did help us deal effectively with increasingly complex geometry. Our approach combines automation and interactivity. From the specification of the building 3D BIM model, our software prototype proceeds through four steps. First, the user divides the building into zones, each zone being defined by a geometrical primitive (i.e. rectangle zone, triangle zone, curved zone, etc.). Next, for each zone a similar case is retrieved from the case library. The retrieval process will generate a first incomplete 3D solution containing some inconsistencies. Next, the incomplete solution is adapted, using constraint programming techniques, to provide a consistent solution. Finally, distribution routes (i.e. ducts and pipes) are generated using constraint programming techniques. The 3D fan coil solution can be modified or improved by the designer, while providing further contribution by concentrating on interactivity. The project has been funded by the Engineering and Physical Sciences Research Council (EPSRC) in the UK

Model-Driven Engineering for Constraint Database Query Evaluation

Data used in applications such as CAD, CAM or GIS are complex, but the techniques developed for their treatment and stor age are not adapted enough to their needs. Examples of these types of data are spatiotemporal, scientific, economic or industrial information, in which data has not a single value because is defined by parameters, variables, functions, equations . . .. These complex data cannot be repre sented nor evaluated with the relational algebra types, then a new, more complex, data type is needed, the Constraint type. Constraint Databases were defined and implemented in order to handle this type of constraint data. When a Constraint Database is implemented, different configura tion parameters can be set up, depending on which database manager is going to be used, which constraint programming tool is going to solve the query evaluation, or which type of constraints can be involved. When some of these parameters are changed, the implementation that supports the evaluation of queries over constraints have to be changed too. For this reason, we propose the use of Model-Driven Engineering to model the queries over Constraint Databases in an independent way of the im plementation and the techniques used to evaluate the queries.Junta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-13714Ministerio de Ciencia y Tecnología TIN2010- 21744-C02-0