ICAPS 2024 Keynote: Hector Geffner
Alexander von Humboldt Professor, Computer Science Department, RWTH Aachen University
Wallenberg Guest Professor, Linkoping University
Learning Representations to Act and Plan
Recent progress in deep learning and deep reinforcement learning (DRL) has been truly remarkable, yet two important problems remain: structural policy generalization and policy reuse. The first is about getting policies that generalize in a reliable way; the second is about getting policies that can be reused and combined in a flexible, goal-oriented manner. The two problems are studied in DRL but only experimentally, and the results are not clear and crisp. In our work, we have tackled these problems in a slightly different manner, developing languages for expressing general policies, and methods for learning them using combinatorial and DRL approaches. We have also developed languages for expressing and learning lifted action models, general subgoal structures (sketches), and hierarchical polices. In the talk, I'll present the main ideas and results, and open challenges. This is joint work with Blai Bonet, Simon Stahlberg, Dominik Drexler, and other members of the RLeap team at RWTH and LiU.
Short Bio
Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, Germany and a Guest Wallenberg Professor at Linköping University, Sweden. Before joining RWTH, he was an ICREA Research Professor at the Universitat Pompeu Fabra, Barcelona, Spain. Hector obtained a Ph.D. in Computer Science at UCLA in 1989 and then worked at the IBM T.J. Watson Research Center in New Work, and at the Universidad Simon Bolivar in Caracas. Distinctions for his work and the work of his team include the 1990 ACM Dissertation Award and three ICAPS Influential Paper Awards. Hector currently leads a project on representation learning for acting and planning (RLeap) funded by an ERC grant.