192.048 Seminar in Knowledge Representation and Reasoning : Neurosymbolic Artificial Intelligence
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2024W, SE, 2.0h, 3.0EC

Merkmale

  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: SE Seminar
  • Format der Abhaltung: Blended Learning

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage

  • relevante Aspekte von Formalismen des Knowledge Representation and Reasonings zu benennen,
  • die aktuelle Forschung zu einem gewählten Thema aus dem Bereich des Knowledge Representation and Reasonings  zu erläutern, sowie
  • relevante Literatur zu einem Thema aus dem Bereich des Knowledge Representation and Reasonings  zu finden.

At the end of the course, the students will be able to read and critique scientific papers. They will be able to analyse the methods proposed in the field of neuro-symbolic AI, and evaluate their strengths and weaknesses. They will be able to prepare an oral presentation of their findings and discuss the studied methods in detail. Finally, they will be able to assess their peers’ work.

Inhalt der Lehrveranstaltung

The seminar will cover topics belonging to a relevant subfield of knowledge representation and reasoning, machine learning, and artificial intelligence (AI): neuro-symbolic AI.

Neuro-symbolic AI is a growing field in knowledge representation and reasoning, machine learning, and AI, whose aim is to augment and combine the strengths of statistical AI with the capabilities of human-like symbolic knowledge and reasoning. Typical problems in neuro-symbolic AI include, e.g., how to integrate background knowledge into machine learning models, how to teach neural models to reason, and how to make machine learning compliant with a set of requirements.

Potential topics for this seminar include:

  • Injecting background knowledge into deep learning models
  • Deep learning with logical requirements
  • Neuro-symbolic AI for large language models
  • Reasoning shortcuts

Methoden

Die Studierenden müssen

  • eine Literaturrecherche zu einem gewählten Thema durchführen,
  • ein Vortragskonzept zum gewählten Thema erstellen, und
  • einen 25 minütigen Vortrag halten.

Students attend the kick-off meeting. In the kick-off meeting, each student is allocated to a topic and a paper. After the kick-off meeting, each student is expected to read the assigned paper and the papers belonging to their topic, and to perform a literature review of the related work. Four weeks after the kick-off meeting, each student is expected to schedule a meeting with their advisor to discuss their findings. In June, there will be a presentation day. During the presentation day, each student is expected to prepare a 25 min presentation where their findings are summarised (which is followed by a 5-10 min discussion). After the presentation, students are given feedback on their presentation and seminar paper. Each student will write a seminar paper (6-7 pages + references) on their topic, where the central ideas and methods presented in the talk are summarised. 

Prüfungsmodus

Prüfungsimmanent

Weitere Informationen

Beachten Sie beim Verfassen der Ausarbeitung bitte die Richtlinie der TU Wien zum Umgang mit Plagiaten: Leitfaden zum Umgang mit Plagiaten (PDF)

Vortragende Personen

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Mi.17:00 - 18:0016.10.2024 https://tuwien.zoom.us/j/8104603001?pwd=WTlBMERDRkJFMVZNU2Z6aHRDUk9Zdz09 (LIVE)Kickoff Meeting

Leistungsnachweis

Prüfungsimmanent

LVA-Anmeldung

Von Bis Abmeldung bis
15.09.2024 10:00 13.10.2024 22:00 11.10.2024 22:00

Curricula

StudienkennzahlVerbindlichkeitSemesterAnm.Bed.Info
066 931 Logic and Computation Gebundenes Wahlfach

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Sprache

Englisch