The international autumn school on Constraint Programming, Combinatorial Optimization and Machine Learning is a joint autumn school of the Association for Constraint Programming (ACP) and 4 working groups of the CNRS research networks on Artificial Intelligence (GDR IA) and Operations Research (GDR RO). These working groups are CAVIAR (Constraints and Learning), RAP (Representation and Algorithms in Practice), ROCT (Operations Research and Constraints), GT2L (Transportation and Logistics). The Artificial and Natural Intelligence Toulouse Institute (ANITI) is also co-organizer. The purpose of the school is, first, to cover the basics of Constraint programming, Discrete Optimization and Machine learning and, second, to present advanced combinations of at least two approaches to attack complex problems. Applications in scheduling and vehicle routing will be addressed. Training sessions and software tutorials will be organized.
The school will take place at LAAS-CNRS, 7 avenue du Colonel Roche 31031 Toulouse France from 24 to 27 November 2020.
There will be two possible participation modes:
• online participation
• onsite participation (limited number of persons subject to future unknown restrictions)
Registration if free in both cases. Please register by following instructions on the school web site.
https://acp-iaro-school.sciencesconf.org <https://acp-iaro-school.sciencesconf.org/>
Speakers (see detailed program and schedule on the school website)
Tias Guns - Vrije Universiteit Brussel
Combinations of machine learning and constraint programming for smart prediction and preference learning for combinatorial optimisation
Nadjib Lazaar, LIRMM Univ. Montpellier
Samir Loudni - LS2N-CNRS, TASC, IMT Atlantique, Nantes
Constraint acquisition and declarative data mining
Joao Marques-Silva, IRIT and ANITI Chair DeepLEVER Toulouse
Machine Learning Meets Automated Reasoning: Explainability, Fairness, Robustness and Model Learning
Axel Parmentier - CERMICS, Ecole des Ponts Paristech
Machine Learning and mathematical programming
Pierre Schaus, UC Louvain
Constraint Programming Solver Technology - Mini CP Tutorial
Simon de Givry and Thomas Schiex, INRAE Toulouse, ANITI DIL chair
Learning and solving Cost Function Networks : Algorithms in Theory and Practice
Mohamed Siala - LAAS-CNRS, INSA, Toulouse
Constraint Programming vs Boolean Satisfiability: similarities, differences, and hybrid frameworks.
Christine Solnon - CITI, INSA de Lyon
Solving Clustering Problems with Constraint Programming
Denys Trystram - LIG, Grenoble INP
Some experiences of using Machine Learning for scheduling jobs in distributed systems
Daniele Vigo - University of Bologna
Integrating Machine Learning into state of the art Vehicle Routing Heuristics
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