Representation Learning for Planning (RLeap)
Opportunities for PhD students and Postdoctoral researchers
Learning for Planning (RLeap) is a project funded
by the European Research Council; Advanced ERC Grant, Agreement:
885107, Years 2020-2025. PI is Hector Geffner; Blai Bonet is the other
senior researcher fully engaged in this research.
The project and our participation in related projects like the EU
funded TAILOR network
(Trustworthy AI: Integrating Learning, Optimization and Reasoning),
and the Swedish Wallenberg
program in AI (WASP), are focused on a research problem that
is at the heart of the current split in AI between data-based
learners and model-based reasoners: the problem of learning symbolic
first-order representations from raw perceptions.
Data-based learners, like those based on deep learning, are popular
because there is plenty of data available, yet they produce black
boxes that lack the flexibility, transparency, and guarantees of
model-based systems. Building models by hand, on the other hand, it
is just too hard.
By showing how to learn meaningful, symbolic models form raw
perceptions, the research is aimed at integrating the benefits of
data-based learners and model-based solvers in the context of
planning, where representations play a key role in expressing,
communicating, achieving, and recognizing goals. First-order
symbolic representations refer to the representations based on
objects and their relations.
The problem of representation learning for planning is largely
unsolved. Two characteristics of deep reinforcement learning,
one of the main approaches for learning how to act, are its ability
to deal with high dimensional perceptual spaces without requiring
any prior knowledge, combined with its inability to produce and
re-use such knowledge. The construction of reusable knowledge
(transfer learning) is a central concern in (deep) reinforcement
learning, but the semantic and conceptual gap between the low level
techniques that are used, and the high-level representations that
are required, is just too large.
For addressing this challenge, new ideas and methods are required
that will build on those of relevant areas that include planning,
learning, knowledge representation, and combinatorial optimization.
The approach to be the interpretations (grounding) of those
representations, and it involves a number of subproblems like:
- learning symbolic representations and their
interpretation from raw perceptions (e.g., images)
- learning hierarchical symbolic representations to enable
planning at different levels of abstraction:
- learning representations to explore, plan, and obtain and
express general plans
- learning structure from suitable combinations of SAT and
- understanding role of attention and partial observability in
learning, grounding, and skill composition
- understanding the theoretical properties of representations
and their relations to planning width
We are seeking highly motivated doctoral students and
postdoctoral researchers eager to make a difference in these
problems with experience in areas such as machine learning,
planning, logic and knowledge representation, combinatorial
optimization and SAT.
The doctoral students and postdoctoral researchers will pursue their
research in the context of these broad goals, on specific themes
that will be a function of their background, skills, and interests,
and the needs of the project.
Ideal candidates should be able to do or learn to do theoretical
and experimental work, logic and algorithms, programming and
"differential programming" (deep learning), and (deep)
reinforcement learning. Good oral and written skills in
English are also required.
Interested PhD candidates should send a CV, transcripts, three
reference letters, and a motivation statement to: email@example.com.
Postdocs should send a CV, contact details of three references, and
a research statement.
Hector is also a Wallenberg Guest Professor at Linköping University (LiU)
within Swedish WASP program in
AI, and there is also the possibility to be part of the
project as a PhD student at LiU with funding from WASP. The call and
application procedure for those slots are posted periodically.
More about RLeap, can be found in the short
version of the project proposals.
Some relevant bibliography from us and other groups, circa 2020:
symbolic representations for planning from the structure of the
B. Bonet and H. Geffner. Proc. ECAI 2020
Projections, and Representation Change for Generalized Planning
B. Bonet, H. Geffner. Proc. IJCAI 2018
Learning features and
abstract actions for computing generalized plans,
B. Bonet, G. Frances, H. Geffner. Proc. AAAI, 2019.
model-based, and general intelligence.
H. Geffner. Proc. IJCAI 2018.
Declarative Action Representations are Overrated: Classical
Planning with Simulators
G Frances, M Ramırez, N Lipovetzky, H Geffner
generalized policies in planning using concept languages
M. Martin and H. Geffner. Proc. KR 2000
deep learning with symbolic artificial intelligence: representing
objects and relations.
M. Garnelo, M. Shanahan. Current Opinion in Behavioral Sciences 29,
skills to symbols: Learning symbolic representations for abstract
G Konidaris, LP Kaelbling, T Lozano-Perez
Journal of Artificial Intelligence Research, 2018
Imitation Learning with Logical Program Policies
Tom Silver, Kelsey R. Allen, Alex K. Lew, Leslie Pack Kaelbling,
to MDP Homomorphisms: Equivariance under Actions
Elise van der Pol, Thomas Kipf, Frans A Oliehoek, Max Welling
Exploration by Self-supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell
expressiveness of Graph Neural Networks.
Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan
Reutter, Juan Pablo Silva
June 11st, 2020