Abstract: During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. The two have close parallels with Daniel Kahneman’s Systems 1 and 2: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this talk, I review learners and solvers, and the challenge of integrating their System 1 and System 2 capabilities, focusing then on our recent work aimed at bridging this gap in the context of action and planning, where combinatorial and deep learning approaches are used to learn general action models, general policies, and general subgoal structures.
Speaker: Hector Geffner is an Alexander Humbolt Professor at RWTH Aachen University, Germany, and a Wallenberg Guest Professor at Linköping University, Sweden. Hector grew up in Buenos Aires and obtained a PhD in Computer Science at UCLA in 1989. He then worked at the IBM T.J. Watson Research Center in New York, at the Universidad Simon Bolivar in Caracas, and at the Catalan Institute of Advanced Research (ICREA) and the Universitat Pompeu Fabra in Barcelona. Hector teaches courses on logic, AI, and social and technological change, and is currently doing research on representations learning for acting and planning as part of the ERC project RLeap 2020-2025.
Recording: Model-free, Model-based, and General Intelligence: Learning Representations for Acting and Planning