Algorithms for Decision Making
Content:
Automated decisionmaking systems are used for many important problems in engineering (e.g. automated driving), medicine (e.g. cancer screening), economics (e.g. portfolio allocation), environmental science (wildfire surveillance), and space travel (e.g. Mars exploration). In their recent book [1], Kochenderfer et al. examine different decisionmaking algorithms from a computational perspective, with a focus on the problem of uncertainty. Uncertainty can be represented using probability distributions and can occur on different levels, such as uncertainty about the outcome of actions or about the underlying world model.
Overall, the different parts of the book cover Probabilistic Reasoning, Sequential Problems, Model Uncertainty, State Uncertainty, and Multiagent Systems. Each part of the book contains then several chapters with a more specific topic describing mathematical problem formulations and computational approaches, often closely related to reinforcement learning and planning.
Each student will cover the topic from a book chapter in a presentation in the seminar, followed by a discussion of the topic with active participation from the whole seminar group.
Learning Outcomes:
• Knowledge on different algorithms and computational approaches for decision making
• Explain the underlying mathematical problem formulations and the implementation of the algorithms to solve them
• Insight into different types of uncertainty and the balancing of multiple objectives
• Discuss practical applications of the theoretical frameworks
• Present the algorithms and mathematical problem formulations to an audience
Examination:
Oral presentation
Lecturers
Prof. Dr. Robert SchmidtLecturer 
(+49) 2343227300 robert.schmidt@rub.de NB 03/68 
Details
 Course type
 Seminars
 Credits
 3
 Term
 Winter Term 2023/2024
Dates
 Seminar

Takes place
every week on Monday from 14:00 to 16:00 in room IC 03/449.
First appointment is on 09.10.2023
Last appointment is on 29.01.2024
Requirements
Knowledge of calculus, linear algebra, and probability concepts. Background in artificial intelligence, e.g. via the course “Introduction to Artificial Intelligence”.
Literature: Kochenderfer, M. J., Wheeler, T. A., & Wray, K. H. (2022). Algorithms for decision making. MIT press. https://algorithmsbook.com/files/dm.pdf
The Institut für Neuroinformatik (INI) is a central research unit of the RuhrUniversität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
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