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Vorlesung Intelligent Agents and Decision Theory

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Semester:Sommersemester 2020
Dozenten:Prof. Dr. Andreas Geyer-Schulz;
Termin:Donnerstag 09:45 - 11:15
Ort:30.28 Seminarraum 4 (R004)


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Important Information

In accordance with the announcement of Prof. Alexander Wanner, KIT Vice President for Higher Education and Academic Affairs, Academic education operations will start punctually on April 20, 2020 with the planned beginning of the lecturing period of the summer semester.

However, digital courses exclusively will be offered until further notice. Dear students, please do not come to the campus for courses or for learning! The KIT departments, central facilities, and business units, and not least the lecturers are doing their best at the moment to let as many courses as possible take place online in parts or completely this summer semester.

Details about the lecture and tutorial (during the Corona measures):

All lecture and tutorial material, as well as the latest annoucements, can be found in Ilias. Further literature is references on this website.

Course Description

The key assumption of this lecture is that the concept of artificial intelligence is inseparably linked to the economic concept of rationality of agents. We consider different classes of decision problems - decisions under certainty, risk and uncertainty - from an economic, managerial and AI-engineering perspective:

From an economic point of view, we analyze how to act rationally in these situations based on classic utility theory. In this regard, the course also introduces the relevant parts of decision theory for dealing with multiple conflicting objectives, incomplete, risky and uncertain information about the world, assessing utility functions, and quantifying the value of information ...

From an engineering perspective, we discuss how to develop practical solutions for these decision problems, using appropriate AI components. We introduce a general, agent-based design framework for AI systems, as well as AI methods from the fields of search (for decisions under certainty), inference (for decions under risk) and learning (for decisions under uncertainty).

Where applicable, the course highlights the theoretical ties of these methods with decision theory.

We conclude with a discussion of ethical and philosophical issues concerning the development and use of AI.


Inhalt Autor
Introduction Geyer-Schulz, Andreas; Schweizer, Marvin

Intelligent Agents Geyer-Schulz, Andreas; Schweizer, Marvin; Kubelka, Lukas

Trade-offs under Certainty Geyer-Schulz, Andreas; Schweizer, Marvin

Search: Linear programming for decisions under certainty Geyer-Schulz, Andreas; Schweizer, Marvin

Decisions under Risk Geyer-Schulz, Andreas; Schweizer, Marvin

Information Systems Geyer-Schulz, Andreas; Schweizer, Marvin

Bayesian Decision Networks Geyer-Schulz, Andreas; Schweizer, Marvin

Inference in Bayesian Networks Geyer-Schulz, Andreas; Schweizer, Marvin

Learning in Bayesian Networks. Basics Geyer-Schulz, Andreas; Schweizer, Marvin