Biography Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert for AutoML at the Bosch Center for Artificial Intelligence. Frank received his PhD from the University of British Columbia (UBC, 2009), supervised by Holger Hoos, Kevin Leyton-Brown and Kevin Murphy. He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank's research focuses on learning and optimization on a meta-level. He worked extensively on algorithm configuration & selection and recently focused on automated machine learning (AutoML).
Abstract The many ingenious approaches underlying state-of-the-art planning systems tend to have complementary strengths; no single planner, heuristic, or parameter setting performs best in all situations, and machine learning can help improve performance. In the first part of this talk, I will survey proven meta-algorithmic approaches & success stories along these lines, including algorithm configuration, algorithm selection, algorithm schedules, and per-instance algorithm configuration. In the second part of the talk, I will then discuss a novel exciting meta-algorithmic framework dubbed dynamic algorithm configuration (DAC) that generalizes all of the meta-algorithmic approaches above and show a first case study of successfully applying DAC to improve planning performance. The DAC framework opens up many new opportunities, and I would be excited if the ICAPS community helps to explore them.
Biography Leslie is a Professor at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founding editor-in-chief of the Journal of Machine Learning Research. Her research agenda is to make intelligent robots using methods including estimation, learning, planning, and reasoning. She is not a robot.
Abstract We, as robot engineers, have to think hard about our role in the design of robots and how it interacts with learning, both in "the factory" (that is, at engineering time) and in "the wild" (that is, when the robot is delivered to a customer). I will share some general thoughts about the strategies for robot design and then talk in detail about some work I have been involved in, both in the design of an overall architecture for an intelligent robot and in strategies for learning to integrate new skills into the repertoire of an already competent robot.
Biography Blai Bonet is a professor in the computer science department at Universidad Simón Bolívar, Venezuela. He received his Ph.D. degree in computer science in 2004 from the University of California, Los Angeles. His research interests are in the areas of automated planning, heuristic search and knowledge representation. He has received several best paper awards or honorable mentions, including the 2009 and 2014 ICAPS Influential Paper Awards, and he is a co-author of the book "A Concise Introduction to Models and Methods for Automated Planning". Dr. Bonet has served as associate editor of Artificial Intelligence and the Journal of Artificial Intelligence Research, conference co-chair of ICAPS-12, program co-chair of AAAI-15, and has been a member of the Executive Council for ICAPS and AAAI.
Abstract Recent work in planning, generalized planning and representation learning is concerned with the problem of learning symbolic representations from traces over small instances. Such representations can be used for different purposes such as to find plans for bigger unseen instances, to do plan and goal recognition, as building blocks for constructing more complex models, etc. In this talk, I will show how SAT solvers can be used to learn first-order STRIPS representations from purely non-symbolic traces, and to learn representations for generalized planning, based on qualitative numerical planning, from symbolic and non-symbolic traces. A common denominator when learning representations from non-symbolic traces is a crisp graph-theoretical problem that is cast as a search problem in a combinatorial space and solved with the help of SAT solvers.