[CogSci] Call for Papers: Learning for Structured Knowledge, Reasoning, and Planning

[Jessica B. Hamrick]

Dear all,

We (Jessica Hamrick/DeepMind, Theophane Weber/DeepMind, Tim
Rocktäschel/UCL&FAIR) are excited to launch a new Research Topic on
"Learning for Structured Knowledge, Reasoning, and Planning" at Frontiers
in Artificial Intelligence, to which we would kindly like to invite you to
consider submitting a paper:

https://www.frontiersin.org/research-topics/25910/learning-for-structured-knowledge-reasoning-and-planning

Humans’ understanding of the world is highly structured: we perceive and
manipulate objects, remember events, construct hierarchies, reason over
plans, develop algorithms, and elaborate formal scientific and mathematical
theories. However, in the majority of “end-to-end” deep learning research,
these structures are only implicitly represented (e.g., in the weights or
activations of a neural network). While such research has made impressive
strides towards more powerful language and perceptual modeling, the
implicit representations employed by these methods may come at a cost to
generalization, data-efficiency, and interpretability.

This Research Topic focuses on ways to address these limitations in deep
learning systems by explicitly incorporating structured knowledge,
reasoning, and planning into the design of sequential decision making
models.

We invite researchers to submit articles on the following topics in the
context of sequential decision making or planning:

- Graph representation learning
- Program synthesis
- Self-supervised learning
- Relational reinforcement learning
- Probabilistic programming
- Neurosymbolic systems
- Knowledge retrieval & integration
- Object-oriented learning
- Causal reasoning
- Exploration and hypothesis testing in reinforcement learning
- Learning world models
- Leveraging world models for decision making

Submissions should generally go beyond metrics like prediction error,
classification accuracy, or reward on standard benchmarks. Instead, we
encourage submissions to emphasize how the work improves on one or more of
the following:

- Zero- or few-shot generalization
- Data-efficient transfer to new tasks
- Interpretability
- Modularity
- Other novel evaluation metrics or environments

We also encourage submissions of review, vision or position pieces on the
above topics. The submission deadline is 31 March 2022 and all submitted
articles are peer reviewed. We hope you will consider submitting, and
encourage you to share the CFP with your network.

Kind Regards,

Jessica Hamrick
Topic Editor, Machine Learning and Artificial Intelligence Section,
Frontiers in Artificial Intelligence

On behalf of the Topic Editors.
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