192.119 Seminar in Artificial Intelligence Algorithmic and Computational Decision Theory
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2023S, SE, 2.0h, 3.0EC


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


Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage...

After successful completion of the course, students are able to critically read and evaluate scientific articles. They are able to work independently to gain an understanding of recently published results, and the methods and proofs behind them. They can communicate the central ideas of their selected topics to non-experts and discuss the value of the presented findings. They know the key features of good oral presentations and the preparation of a corresponding seminar paper. Moreover, they will understand the relevant results of the topics of fellow students.

Inhalt der Lehrveranstaltung

Participants of the seminar work on selected topics from a relevant subfield of Artificial Intelligence: decision making and computation.
Decision making problems arise from a diverse range of research areas such as social choice theory, game theory, political sciences, computer sciences, and multi-agent systems. Typical problems include how to aggregate individual preferences or judgments to reach a consensus, how to fairly allocate a set of resources to some agents, how to optimally assign schools or colleges to students based on their preferences, or how to recommend potential interesting products such as movies to a user based on her and others’ past and current preferences. During the seminar we explore relevant topics in decision making, and discuss mathematical and axiomatic properties as well as algorithmic and complexity issues of societal decision making problems.

Potential topics include:
- preference aggregation and (multi-winner) voting rules,
- restricted preference domains and their applications,
- matching under preferences,
- strategies and equilibria in game theory,
- cake cutting protocols,
- fair allocation of resources,
- judgment aggregation,
- simple games, and
- the measurement of political power.


  • Student participants read pre-selected articles, perform literature research for related work and discuss their results with their advisor in the first 4 weeks after topic assignment.
  • Each student prepares presentation slides of his/her selected topic.
  • 4 weeks before the due presentation slot, he/she discusses the concrete structure of the presentation with his/her advisor.
  • 2 weeks before the due presentation slot, he/she discusses the slides and plan of the write-up with his/her advisor.
  • Each participating student presents his/her topic to the other participants (30mins each), asks questions and gives feedback to the presentations of the fellow students.
  • He/she also writes a seminar paper (6-7 pages + references) on his/her topic where central ideas and methods presented in the talk are summarized.


Schriftlich und Mündlich

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


LVA Termine

Mo.10:00 - 12:0013.03.2023Seminarraum FAV 05 (Seminarraum 186) Introductory session
Mo.10:00 - 16:0022.05.2023Seminarraum FAV 01 B (Seminarraum 187/2) Presentation (on-site)
Mo.10:00 - 14:0029.05.2023Seminarraum FAV 01 C (Seminarraum 188/2) AI Seminar


5% Literature research + 10% meetings with the advisor + 45% presentation + 40% seminar paper.


Von Bis Abmeldung bis
14.02.2023 00:00 09.03.2023 23:00 12.03.2023 23:55


Thanks for your registration. The time of the first introductory meeting will be announced shortly.


066 931 Logic and Computation Gebundenes Wahlfach
066 936 Medizinische Informatik Gebundenes Wahlfach
066 937 Software Engineering & Internet Computing Gebundenes Wahlfach


Es wird kein Skriptum zur Lehrveranstaltung angeboten.