Einladung Informatik Kolloquium Hector Geffner / Invitation Computer
by van der Aalst, Willibrordus
***********************************************************************
*
* Informatik-Kolloquium / Computer Science Colloquium
*
***********************************************************************
Presenter: Hector Geffner (ICREA and Universitat Pompeu Fabra, Barcelona, Spain & Linköping University, Sweden)
Title: Language-based representation learning for acting and planning
Time: Monday 27-6-2022, 14.30
Location: Room 9222, E3 (Informatik-Zentrum, Erweiterungsbau E3,
https://www.informatik.rwth-aachen.de/cms/informatik/Fachgruppe/Informatik-…)
Abstract:
Recent breakthroughs in AI have shown the remarkable power of deep
learning and deep reinforcement learning. These developments, however,
have been tied to specific tasks, and progress in out-of-distribution
generalization has been limited. While it is assumed that these
limitations can be overcome by incorporating suitable inductive biases
in neural nets, this is left vague and informal, and does not provide
meaningful guidance. In this talk, I articulate a different learning
approach where representations are learned over domain-independent
target languages whose structure and semantics yield a meaningful and
strongly biased hypothesis space. The learned representations do not
emerge from biases in a low level architecture but from a general
preference for the simplest hypothesis that explain the data. I
illustrate this general idea by considering three concrete learning
problems in AI planning: learning general actions models, learning
general policies, and learning general subgoal structures ("intrinsic
rewards"). In all these cases, learning is formulated and solved as a
combinatorial optimization problem although nothing prevents the use of
deep learning techniques instead. Indeed, learning representations over
domain-independent languages with a known structure and semantics
provides an account of what is to be learned, while learning
representations with neural nets provides a complementary account of how
representations can be learned. The challenge and the opportunity is to
bring the two approaches together.
Reference: Target languages (vs. inductive biases) for learning to act
and plan. Hector Geffner. AAAI 2022. https://arxiv.org/abs/2109.07195
Short Bio:
Hector Geffner is an ICREA Research Professor at the Universitat Pompeu
Fabra (UPF) in Barcelona, Spain, and a Wallenberg Guest Professor at
Linköping University. He 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 NY, USA, and at the Universidad Simon Bolivar, in
Caracas. 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.
Es laden ein: die Dozentinnen und Dozenten der Informatik
<http://www.vdaalst.com/>
prof.dr.ir. Wil van der Aalst ● <http://www.vdaalst.com/> www.vdaalst.com ● @wvdaalst
