Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN462Fundamentals of Decision Making3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To teach students principles and practice of decision making for autonomous agents, and robots in particular. The emphasis is on understanding how to equip robots with the capacity to autonomously make decisions about how to interact with a dynamic environment.
Course Content Rationality, decision theory, probabilistic reasoning, dynamic programming, Markov decision processes, planning, optimization, reinforcement learning, learning from demonstration, and explainability and behavioural aspects for decision making.
Course Methods and Techniques Lecture, Discussion, Question and Answer, Problem Solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Prof. Ebru Akçapınar Sezer
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources ?Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis?, M. Mitzenmacher, E. Upfal, Cambridge University Press, 2017. ?Dynamic Programming and Optimal Control?, D.P. Bertsekas, Vols. I & II, Athena Press, 2017. ?Decision Making Under Uncertainty: Theory and Application?, Kochenderfer, MIT Lincoln Laboratory Series, 2015. ?Reinforcement Learning: An Introduction?, Sutton and Barto, MIT Press, 2015.
Course Notes “Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis”, M. Mitzenmacher, E. Upfal, Cambridge University Press, 2017.

“Dynamic Programming and Optimal Control”, D.P. Bertsekas, Vols. I & II, Athena Press, 2017.

“Decision Making Under Uncertainty: Theory and Application”, Kochenderfer, MIT Lincoln Laboratory Series, 2015.

“Reinforcement Learning: An Introduction”, Sutton and Barto, MIT Press, 2015.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 2 % 50
Attendance 1 % 10
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 6 84
Preparation for Midterm Exam 2 15 30
General Exam Preparation 1 24 24
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Students will be able to describe what kinds of decisions robots need to make in realistic autonomy domains. Students will be able to use mathematical models and computational methods to program such decision making behaviour.
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction: Robots, Models, and Rationality
2 Model-based Control and Architectures for Task Encoding
3 Dynamic Programming and Decision Theory
4 Game Theory and Decentralised Decision Making
5 Probabilistic Methods for Robotics
6 Midterm Exam
7 Reinforcement Learning
8 Learning from Demonstration
9 Optimization Techniques
10 Performance Guarantees in Robotics
11 Midterm Exam
12 Safety, Security and Privacy
13 Explainability for Decision Making
14 Behavioural Aspects of Human Choice
15 Preparation to Final Exam
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 3 4 4 4 4 4 2 3 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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