"Empirical Model Learning for Sustainability Challenges"


Sustainability challenges often involve reasoning, managing and deciding on complex, interconnected global systems spanning across different sectors: examples include energy networks, financial markets, natural ecosystems, and cities.

In principle, using optimization methods to support high-level decision making activities in those contexts may lead to dramatically more sustainable and efficient policies. In practice, the kind of systems considered in computational sustainability can be an optimizer's worst nightmare: they typically involve complex infrastructures, organizations, laws and processes; they influence and are influenced by the environment, and are strongly perturbed by human behavior; they feature multiple actors, often self-interested and with conflicting objectives. The classical, expert-driven, modeling approach used in optimization has a very hard time coping with situations where the experts themselves are incapable of providing precise, non-ambiguous definitions of the problem constraints and goals.

We argue that dealing with systems of such a complexity calls for a strong integration of data science and optimization, raising interest in technique that try and bridge the gap between the two fields. Empirical Model Learning (EML) is one such technique: it is a methodology for learning model components directly from data and for actively using these components to prune the search space or guiding the solution process.

We basically have to learn relations between decidables (alternative decisions we can take) and a observables of interest. The data for the learning process can come from historical measurements or be collected by running simulations. These relations can be extracted in the form of classical Machine Learning models (e.g. Neural Networks, Decision Trees), and EML defines methods to cast such models into constraints and objective functions that can be readily incorporated into existing optimization technology.

The EML methodology is relatively recent and still an active research topic, but it has already been proved applicable to practical problems. In this talk, we will provide examples in the field of computational sustainability, along with empirical models learnt using different Machine Learning methods and integrated in different optimization techniques (Constraint Programming, Mixed Integer Non-Linear Programming, and SMT).


Michela Milano, PhD,  is full professor of Intelligent Systems at the University of Bologna. Her research activity covers methodologies and techniques for the design and development of decision support systems in several areas, including sustainability problems, smart cities, policy making, industrial applications, and high performance computing. She is member of the executive committee of the Italian Association of Artificial Intelligence, board member of the European Association of Artificial Intelligence EurAI and Councilor of the Association for the Advancements of Artificial Intelligence AAAI. She has a broad research activity in the area of Artificial intelligence as demonstrated by over 160 publications in international journals and conferences. She is Editor in Chief of the Constraints Journal, Area Editor of INFORMS Journal on Computing and Constraint Programming Letters.

Michela Milano has coordinated and participated in many national and EU-funded projects , like ePolicy in the field of sustainable policy making, COLOMBO for improving traffic in urban areas,DAREED for optimizing energy districts, OPRECOMP on transprecision computing. Michela Milano has been the recipient for a Google Faculty Award in 2016 for the integration of deep learning techniques in combinatorial models. She has many collaborations and research projects with SME and big industrial players. She is co-founder of the university spin-off MindIT.