Hybrid Learning and Reasoning

Seminar an der Universität des Saarlandes, Fachrichtung Informatik,  LSF 140201


This seminar (HyLEAR) is concerned with selected hybrid intelligent systems that combine techniques from subsymbolic learning (deep learning within area of machine learning) with symbolic techniques for reasoning, planning or learning. Both types of AI techniques have their strengths and limitations. While deep learning systems have been quite successfully applied to, for example, pattern recognition, image interpretation, speech recognition and translation, they can be characterized as overly data hungry, susceptible to adversarial attacks, opaque (non-interpretable by humans), and not informed by general principles such as causality or commonsense and domain knowledge. The successes of symbolic reasoning techniques (“good old fashioned AI”) are in such applications as automated (human-understandable, traceable) planning, diagnosis, design tasks, and question answering by cognitive virtual assistants but are often quite limited by the need of expensive, explicit knowledge acquisition and modelling, inefficient logic-based reasoning, and instability in the presence of noisy data. There is a consensus in the AI community that symbiotic, profound integration or combination of machine learning and reasoning is essential for human-level AI in general. [1,2]

In this seminar, we will take a closer look at selected techniques and systems for hybrid learning and reasoning or planning, and discuss their strengths and weaknesses. The seminar type is classic in the sense that registered participants will present assigned topics, and discuss the strength and weaknesses of presented approaches. In addition, there will be two dedicated opponents for each presentation of an assigned topic. Participation in the seminar will be graded; please check the requirements page in this regard.

The seminar counts 7 ECTS credit points (CS).


This seminar aims primarily at advanced master students in Computer Science who preferably hold a B.Sc. degree in this or related field. Good knowledge in AI (introductory course on AI covering symbolic knowledge representation and reasoning, machine or deep learning, automated planning) is required. Selected background references are given on the topic page and expected to be read and utilized by registered participants as appropriate. Attendance of the seminar without registration (no presentation and certificate) by anyone who is interested in the topics is, of course, very much welcome. The seminar language is English or German depending on the audience.

Date and Location:

The seminar is held on Wednesdays from 10:15am to 12:00.

PLEASE NOTE: First session (introduction with topic assignments) on Wednesday 9.11.2022

The seminar takes place at  DFKI Saarbrücken,  SIC Bldg. D3.2, in room “Leibniz”  (please ask for directions at DFKI reception desk)  or  virtually via Zoom . Please check the schedule frequently and in time for updates in this regard.

Registration and Topic Assignments:

Registration request with preferences to the seminar is through the central SIC seminar system until October 26, 2022. Central assignment of students to the seminar will then automatically be done by the system on October 30, 2022. If there are free slots available after that date, students can apply for these slots by e-mail to and decision by the seminar organizers.

Assignment of seminar presentation topics will be done during the first session of the seminar on Wednesday, November 9, 2022, 10:15am - 12. It is recommended to carefully check the reference papers and preliminary presentation dates of topics (see schedule) before registering to the seminar in the central SIC seminar system.

[1] van Harmelen, F. & Teije, A.T. (2019): A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems. Journal of Web Engineering, 18(1-3)
[2] van Bekkum, M., de Boer, M., van Harmelen, F. et al. (2021): Modular design patterns for hybrid learning and reasoning systems: A taxonomy, patterns and use cases. Applied Intelligence.