Titlebook: Data Mining and Constraint Programming; Foundations of a Cro Christian Bessiere,Luc De Raedt,Dino Pedreschi Book 2016 Springer Internationa

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ICON Loop Health Show CaseIn this document we describe the health show case for the ICON project. This corresponds to Task 6.2 in WP 6 of the Description of Work for the project. The description provides a high-level abstraction, detailed description of the interfaces between modules, and a description of sample data.

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Learning Constraint Satisfaction Problems: An ILP Perspectiveems are the underlying basis for constraint programming and there is a long standing interest in techniques for learning these. Constraint satisfaction problems are often described using a relational logic, so inductive logic programming is a natural candidate for learning such problems. So far, the

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Learning Modulo Theories. Being able to precisely specify all constraints and their respective importance beforehand is often infeasible for the most experienced designer, let alone for a typical decision maker. In this chapter we discuss Learning Modulo Theories (LMT), a learning framework capable of dealing with hybrid d

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Algorithm Selection for Combinatorial Search Problems: A Surveylly relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatoria