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Neural-Symbolic Computing

You can register for the seminar at until April 17th 23:59 CET.

The way our brain forms thoughts can be classified into two categories (according to Kahneman in his book “Thinking Fast and Slow”):

System 1: fast, automatic, frequent, stereotypic, unconscious.

  • Is this a cat or a dog?
  • What does this sentence mean in English?

System 2: slow, effortful, logical, conscious.

  • 17*16 = ?
  • If a -> b does b -> a?

The traditional view is that deep learning is limited to System 1 type of reasoning. Mostly because of the perception that deep neural networks are unable to solve complex logical reasoning tasks reliably. Historically, applications of machine learning were thus often restricted to sub-problems within larger logical frameworks, such as resolving heuristics in solvers.

In this seminar, we will explore new research that shows that deep neural networks are, in fact, able to reason on “symbolic systems”, i.e., systems that are built with symbols like programming languages or formal logics.

Example Topics:

  • What are your chances against an AI in a programming competition?
  • Is it possible to teach temporal logics to neural networks?
  • Can neural networks learn the intuition of mathematicians to improve automated theorem proving?


Participants should have strong interest in logical reasoning and/or machine learning. There is, however, no formal prerequisite.


The structure of the seminar is as follows:

In the first week of the seminar, you will choose a research paper on neural-symbolic computing.

Group Phase: Depending on your choice you will be assigned to a background topic of machine learning such as graph neural networks. In a team of three students, you will then prepare an informal lecture on your topic and the following discussion; presenting the basics to your fellow students. Each topic is assigned to an advisor that will help you with your preparations. This informal lecture will not be graded, so you can see it as a rehearsal. The group phase, thus, gives you the necessary foundations for the following individual phase.

Individual Phase: You will prepare, with the help of your advisor, a research talk on the paper you have chosen presenting the findings of the paper to your fellow students. This talk is weighted most in your final grade.

Project Phase: You will be given a neural-symbolic computing task. Each team is required to solve this problem with the methods explored in this seminar, for example by applying deep neural network architectures of your area. We do not expect own implementations of the discussed methods. You can use available libraries. This project has to be passed and it will not be graded.


Kick-off meeting: Tuesday, April 23rd, at 4:00 pm

Weekly meetings: Tuesdays at 4:00 pm

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