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* Informatik-Kolloquium / Computer Science Colloquium

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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

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

prof.dr.ir. Wil van der Aalst ● www.vdaalst.com ● @wvdaalst