The Inductive Constraint Programming Loop

Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn't exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other.

Authors

• Christian Bessiere
• Luc De Raedt KU Leuven Örebro University
• Tias Guns KU Leuven
• Lars Kotthoff University of Wyoming, Laramie, WY, USA
• Mirco Nanni
• Siegfried Nijssen
• Barry O'Sullivan
• Anastasia Paparrizou
• Dino Pedreschi
• Helmut Simonis

Keywords

• Artificial intelligence
• Programming
• Loss measurement
• Data mining
• Machine learning
• Intelligent systems
• Constraint optimization
• Loop Inductance
• Constraint Programming
• Inductive Programming
• Optimization Problem
• Optimization Criteria
• Constrained Optimization
• Field Of Machine Learning
• Task Scheduling
• Constraint Satisfaction
• Constraint Satisfaction Problem
• Constraint Optimization Problem
• Least-squares
• Use Of Resources
• Learning Problem
• Regression Problem
• Training Examples
• Changing World
• Task Duration
• Previous Solution
• Global Constraints
• Network Constraints
• Hypothesis Space
• Task Properties