Inductive Constraint Programming

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In industry, society and science advanced software is used for solving planning, scheduling and resource allocation problems, collectively known as constraint satisfaction or optimization problems. At the same time, one continuously gathers vast amounts of data about these problems. This project starts from the observation that current software typically does not exploit such data to update schedules, resources and plans. It aims at developing a new approach in which gathered data is analysed systematically in order to dynamically revise and adapt constraints and optimization criteria. Ultimately, this could create a new ICT paradigm, called Inductive Constraint Programming, that bridges the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution of constraint satisfaction problems, but also to employ declarative constraint programming principles in data mining and machine learning.
Grossi, V., T. Guns, A. Monreale, M. Nanni, and S. Nijssen, "Partition-Based Clustering Using Constraint Optimization", Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach: Springer International Publishing, pp. 282–299, 2016.
Kotthoff, L., M. Nanni, R. Guidotti, and B. O’Sullivan, "Find Your Way Back: Mobility Profile Mining with Constraints", Principles and Practice of Constraint Programming, Cork, Springer International Publishing, 2015.
Grossi, V., A. Monreale, M. Nanni, D. Pedreschi, and F. Turini, "Clustering Formulation Using Constraint Optimization", Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers: Springer Berlin Heidelberg, 2015.
Milli, L., A. Monreale, G. Rossetti, D. Pedreschi, F. Giannotti, and F. Sebastiani, "Quantification in Social Networks", International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Paris, France, IEEE, 2015.
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Image by Faruk Ateş CC BY-NC 2.0, via Flickr
Acronym
ICON
Web Site
Start Date
1 January 2012
End Date
30 June 2015
Type
Affiliation
Department of Computer Science, University of Pisa (DI-UNIPI)