Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN463Autonomous Multiagent Systems3+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 the basic concepts of autonomous multi-agent systems
Course Content Autonomous agents, Agent architectures, Multiagent systems, Agent communication and Teamwork, RoboCup case studies, Swarms and self-organization, Applications, Game theory, Multiagent learning, Distributed rational decision making, Auctions, Agent modeling
Course Methods and Techniques Lecture
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Özgür Erkent
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Michael Wooldridge. 2009. An Introduction to Multiagent Systems (2nd ed.). Wiley Publishing.
Course Notes Michael Wooldridge. 2009. An Introduction to Multiagent Systems (2nd ed.). Wiley Publishing.


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 1 % 30
Assignment 3 % 30
Final examination 1 % 40
Total
5
% 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 3 42
Assignments 3 20 60
Preparation for Midterm Exam 1 20 20
General Exam Preparation 1 20 20
Total Work Load   Number of ECTS Credits 6,13333333333333 184

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Identify and discuss the characteristics of agent-based systems
2 Program actual agents in one multi-agent environment.
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction
2 Autonomous agents
3 Agent architectures
4 Multiagent systems
5 Agent communication and Teamwork
6 RoboCup case studies
7 Swarms and self-organization
8 Applications
9 Game theory
10 Game theory II
11 Multiagent learning
12 Distributed rational decision making
13 Auctions
14 Agent modeling
15 Final exam preparation
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 5 5 5 5 5 2 1 2 1 1
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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