Preference Learning and Ranking (SS 2024)

The lecture will give a general introduction to methods for modeling and learning preferences, with a specific focus on methods for ranking. The topic of preferences has recently attracted considerable attention in artificial intelligence (AI) research, notably in fields such as autonomous agents, constraint satisfaction, planning, and information retrieval. Preferences provide a means for specifying desires in a declarative way, which is a point of critical importance for AI. Drawing on past research on knowledge representation and reasoning, AI offers qualitative and symbolic methods for treating preferences that can reasonably complement hitherto existing approaches from other fields, such as decision theory. Needless to say, however, the acquisition of preference information is not always an easy task. Therefore, not only are modeling languages and suitable representation formalisms needed, but also methods for the automatic learning, discovery, modeling, and adaptation of preferences.

Prerequisites

• Mathematical fundamentals (linear algebra, calculus)
• Algorith fundamentals (algorithms and data structures)
• Probability and statistics fundamentals
• Programming

Links

General Information

Lecturer:

Eyke Hüllermeier

Language:

English

Time and place:

Lecture	Tuesday	16-18
Exercises 1	Thursday	14-16
Exercises 2	Friday	10-12

Scope:

2 SWH Lecture +2 SWH Exercises

Target audience:

• Informatics
• Mathematics
• Statistics
• Data science

Assistants:

• Timo Kaufmann
• Tobias Oberkofler