Vorlesung Intelligent Agents and Decision Theory
Semester: | Summer Term 2024 |
Lecturer: | Prof. Dr. Andreas Geyer-Schulz; |
Appointment: | Donnerstag 09:45 - 11:15 |
Location: | Geb. 11.40 Raum 221 |
SWS: | 2 |
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Content
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 |