Efficient Algorithms For Strong Local Consistencies And Adaptive Techniques In Constraint Satisfaction Problems
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Efficient Algorithms for Strong Local Consistencies and Adaptive Techniques in Constraint Satisfaction Problems
Constraint programming is a successful technology for solving a wide range of problems in business and industry which require satisfying a set of constraints. Central to solving constraint satisfaction problems is enforcing a level of local consistency. In this thesis, we propose efficient filtering algorithms for enforcing strong local consistencies. In addition, since such filtering algorithms can be too expensive to enforce all the time, we propose some automated heuristics that can dynamically select the most appropriate filtering algorithm. Published by AI Access, a not-for-profit publisher of open access texts with a highly respected scientific board. We publish monographs and collected works. Our texts are available electronically for free and in hard copy at close to cost.
Data Mining and Constraint Programming
A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.
Qualitative Representation of Spatial Knowledge
Author: Daniel Hernandez
language: en
Publisher: Springer Science & Business Media
Release Date: 1994-06-28
This book develops, for the first time, a qualitative model for the representation of spatial knowledge based only on locative relations between the objects involved. The core of this book is devoted to the study of qualitative inference methods that take into account the rich structure of space. These methods can be applied to quite a number of areas characterized by uncertain or incomplete knowledge, as for example geographic information systems, robot control, computer-aided architectural design, and natural language information systems.