Michela Milano (University of Bologna)  

Empirical Model Learning: boosting optimization through machine learning

One of the biggest challenges in the design of decision support and optimization tools for complex, real-world, systems is coming up with a good combinatorial model. The traditional way to craft a combinatorial model is through interaction with domain experts: this approach provides model components (objective functions, constraints), but with limited accuracy guarantees. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization.

In this talk, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining decision model components that link decision variables and observables, using data either extracted from a predictive model or harvested from a real system. We show how to ground EML on a case study of thermal-aware workload allocation and scheduling. We show how to encapsulate different machine learning models in a number of optimization techniques.

We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models.

The talk is part of a joint session with CPAIOR.

Bio: Michela Milano is full professor at the Department Computer Science and Engineering of the University of Bologna. She received her Ph.D. in Computer Science in 1998 with a thesis on Constraint Programming. Her research interests cover the area of hybrid optimization, a multi-disciplinary field at the cross-road of computer science and applied mathematics, optimization for embedded system design and computational sustainability.   

Richard Korf (UCLA)  

Richard Korf is a Professor of computer science at the University of California, Los Angeles. He received his B.S. from M.I.T. in 1977, and his M.S. and Ph.D. from Carnegie-Mellon University in 1980 and 1983, respectively, all in computer science. His research is in the areas of problem-solving, heuristic search, and planning in artificial intelligence. He is the author of "Learning to Solve Problems by Searching for Macro-Operators" (Pitman, 1985). He serves on the editorial boards of Artificial Intelligence, and the Journal of Applied Intelligence. Dr. Korf is the recipient of a 1985 IBM Faculty Development Award, a 1986 NSF Presidential Young Investigator Award, the first UCLA Computer Science Department Distinguished Teaching Award in 1989, the first UCLA School of Engineering Student's Choice Award for Excellence in Teaching in 1996, the Lockheed Martin Excellence in Teaching Award in 2005 and the Artificial Intelligence Journal Classic Paper Award in 2016. He is a Fellow of the Association for the Advancement of Artificial Intelligence.

Subbarao Kambhampati (Arizona State University)  

Subbarao Kambhampati (Rao) is a professor of Computer Science at Arizona State University, and is the current president of the Association for the Advancement of AI (AAAI), and a trustee of the Partnership for AI. His research focuses on automated planning and decision making, especially in the context of human-aware AI systems. He is an award-winning teacher and spends significant time pondering the public perceptions and societal impacts of AI. He was an NSF young investigator, and is a fellow of AAAI and AAAS. He served the AI community in multiple roles, including as the program chair for IJCAI 2016 and program co-chair for AAAI 2005. Rao received his bachelor’s degree from Indian Institute of Technology, Madras, and his PhD from University of Maryland, College Park. More information can be found at rakaposhi.eas.asu.edu.

The talk is part of the ICAPS Public Event.

Hannah Bast (University of Freiburg)  

Hannah Bast did her PhD at the Max-Planck-Institute for Informatics / University of Saarland. Since 2009, she is a professor of computer science at the University of Freiburg at the foot of the beautiful black forest mountains. She is a big fan of easy to use and powerful information systems of all kinds. She has received various research and teaching awards. One of the algorithms from her work is used for public transit routing on Google Maps. Her CompleteSearch engine powers the bibliography search on DBLP, and many of the features from that engine have become commonplace in search nowadays. She also works on natural language processing and is convinced of the great potential of deep learning for this and other problems. She believes that the world has more pressing problems than whether AI will eventually take over.

News

December 2017 - The workshop program has been announced.
December 2017 - Accommodation details are now available.
December 2017 - The calls for nominations for the Best Dissertation Award and the Influential Paper Award are online.
November 2017 - Invited Speakers announced
October 2017 - Call for Tutorial Proposals and Workshop Proposals available
September 2017 - Call for Papers released
June 2017 - ICAPS 2018 announced

Important dates

Abstract submission November 17, 2017
Paper submission November 21, 2017
Author notification January 29, 2018
Summer School June 20 - 23, 2018
Conference June 24 - 29, 2018

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