Karlsruhe Institute of TechnologyKarlsruhe Institute of Technology


Vorlesung Intelligent Agents and Decision Theory

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Semester:Summer Term 2024
Lecturer:Prof. Dr. Andreas Geyer-Schulz;
Appointment:Donnerstag 09:45 - 11:15
Location:Geb. 11.40 Raum 221


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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 may also discuss ethical and philosophical issues concerning the development and use of AI.

Course material

Content Author
Introduction Geyer-Schulz, Andreas

Intelligent Agents Geyer-Schulz, Andreas

Trade-offs under Certainty Geyer-Schulz, Andreas

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

Decisions under Risk Geyer-Schulz, Andreas

Information Systems Geyer-Schulz, Andreas

Bayesian Decision Networks Geyer-Schulz, Andreas

Inference in Bayesian Networks Geyer-Schulz, Andreas

Learning in Bayesian Networks. Basics Geyer-Schulz, Andreas

Learning in Bayesian Networks. Algorithms Geyer-Schulz, Andreas