Machine Learning for Modelling and Decision Making in Complex Physical Domains

Presentation Date: 

Monday, November 4, 2019


Centre for Artificial Intelligence Decision-making and Action (CAIDA), University of British Columbia, Vancouver, BC


My lab at the University of Waterloo carries out work on a variety of topics within Artificial Intelligence and Machine Learning with a focus on using real world problems to discover computational challenges for modelling of uncertainty, dealing with large or streaming datasets, learning predictive models and enabling decision making.  In this talk I will provide a brief overview of a few ongoing projects where these challenges arise from different sources.  In combustion modelling for energy production and engine design, standard practices are to simplify complex turbulence and other physical dynamics down to simple lookup tables. We have introduced a new approach for using supervised learning to create more a powerful and compact way of doing this.  In physical chemistry, automated material design presents a computational challenge arising from the combinatorial size inherent in a multi-step process of continuous actions each relying on a detailed physics model. I will describe a new project we have looking at using Deep Reinforcement Learning to automate parts of this problem.  In multi-agent domains where there are many human decision makers and where decision support systems or full automation are desirable, computational challenges can arise from communication needs as many individuals interact as well as their needs for communication or their relation to each other as they seek to optimize their goals.  We have been developing several approaches for addressing this as a Multi-Agent Reinforcement Learning (MARL) problem in domains such as autonomous driving and management of forest fires.


Talk webpage: Centre for Artificial Intelligence, Decision Making and Action (CAIDA).