192.119 Seminar in Artificial Intelligence Algorithmic and Computational Decision Theory
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, SE, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: SE Seminar
  • Format: Presence

Learning outcomes

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.

Subject of course

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.

Teaching methods

  • 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.

Mode of examination

Written and oral

Additional information

Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: Directive concerning the handling of plagiarism (PDF)

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Mon10:00 - 12:0013.03.2023Seminarraum FAV 05 (Seminarraum 186) Introductory session
Mon10:00 - 16:0022.05.2023Seminarraum FAV 01 B (Seminarraum 187/2) Presentation (on-site)
Mon10:00 - 14:0029.05.2023Seminarraum FAV 01 C (Seminarraum 188/2) AI Seminar

Examination modalities

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

Course registration

Begin End Deregistration end
14.02.2023 00:00 09.03.2023 23:00 12.03.2023 23:55

Registration modalities

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

Curricula

Study CodeObligationSemesterPrecon.Info
066 931 Logic and Computation Mandatory elective
066 936 Medical Informatics Mandatory elective
066 937 Software Engineering & Internet Computing Mandatory elective

Literature

No lecture notes are available.

Language

English