The Inductive Constraint Programming Loop
This addresses the gap between data mining/machine learning and constraint programming for real-world optimization problems like planning and scheduling.
The paper tackles the problem of constraint programming software not utilizing gathered data to update schedules, resources, and plans, proposing the Inductive Constraint Programming loop to dynamically revise constraints and optimization criteria based on data analysis.
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.