After successful completion of the course, students are able to...

• ... describe the role of preferences in Artificial Intelligence.
• ... represent preference data in computer systems.
• ... integrate preferences in selected knowledge representation formalisms.
• ... apply and analyze various aggregation methods.
• ... contribute to scientific research with a team of experts.

Preferences are ubiquitous. They determine our decisions, from simple everyday decisions (such as having tea or coffee, ordering another glass of beer) to much more fundamental decisions (such as accepting a job offer, getting married, political participation). Preferences also play a tremendous role in many applications, such as logic programming, multi-agent systems, diagnosis etc. Given this, it is far from surprising that various subfields of artificial intelligence (AI) have come up with models for representing preferences and for reasoning and decision making based on these models. Whereas classical decision theory is based on a numerical representation of preferences (utilities), more recently qualitative as well as mixed qualitative/quantitative models of preferences have been a major focus of research.

The course will present the most influential preference models developed in various subfields of AI. In particular, it will cover preferences in constraint reasoning, CP nets, preferences in nonmonotonic reasoning, and the role of preferences in belief change. Moreover, we will explore the challenges of joint decision making based on preferences, a central problem in multi-agent systems.

The course is based on two main parts. The first part will consist of 6 lectures which provide the necessary background and foundational material as well as an introduction to current research topics. Lectures will be held in Zoom. In the second part, students have to apply the concepts and techniques presented in the lecture within a small project, which can either be concerned with a theoretical question or about implementation. Hereby, students will thus actively work with up to date literature and participate in current research conducted at our group.

ECTS breakdown: 3 ECTS = 75 Hours

Lecture 15h
Exercise sheet/Literature review 15h
Project 45h

ECTS breakdown: 3 ECTS = 75 Hours

Lecture 15h
Exercise sheet/Literature review 15h
Project 45h

1. Exercise sheet based on lecture material
2. Completion of a project

The course is for master and PhD students with background in formal logic and complexity theory. Some experience with knowledge representation and/or artificial intelligence will be helpful, but is not a necessary precondition for successful participation.