2015/10/12 by Christian Bessière, Bessiere, Christian, Luc De Raedt +17
Computer Science · #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Rough Sets and Fuzzy Logic #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1510.03317
openalex publication_date 2015/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.