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Information
| Code | CENG0054 |
| Name | Multi-agent Systems |
| Term | 2024-2025 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Mehmet SARIGÜL |
Course Goal / Objective
The goal of a multi-agent systems course is to introduce students to the concepts and techniques used in the design and analysis of systems that involve multiple interacting agents. Multi-agent systems are systems that consist of multiple autonomous entities, each with its own goals, beliefs, and capabilities, that interact with each other to achieve individual and collective objectives.
Course Content
This course covers Normal Form Games, Normal Form Definitions, Dominant Strategies, Pareto Optimality, Mixed Strategies and Nash Equilibrium. Maxmin Strategy, Minimax regret, Iterative Removal of Dominant Strategies, Computing Nash Equilibrium, Complexity of Nash Equilibrium and Compact Representation. Extensive Form Definitions, Centipede Game, Backward Inductions, Inperfect Information, and Subgame Perfect Equilibrium, Finite Repetitive Games, Infinitely Repetitive Games, Stochastic Games, Learning in Repetitive Games, Folk Theorems. Coalitionary Game Theory, Shapley Value, Nucleus and Bayes Games. Rational Learning, Reinforcement Learning, Replicator Dynamics and Evolutionarily Stable Strategies, and Congestion Games.
Course Precondition
Knowledge of basic programming, linear algebra, and probability theory.
Resources
Shoham, Yoav, and Kevin Leyton-Brown. Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press, 2008.
Notes
Shoham, Yoav, and Kevin Leyton-Brown. Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press, 2008.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understanding of the fundamental concepts of multi-agent systems |
| LO02 | Familiarity with different approaches to modeling and analyzing multi-agent systems |
| LO03 | Ability to design and implement multi-agent systems |
| LO04 | Ability to analyze the performance of multi-agent systems |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 3 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 2 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 1 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to multi-agent systems, agents and environments | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Coordination and cooperation among agents | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Game theory for multi-agent systems | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Distributed decision-making and consensus protocols | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Multi-agent learning and reinforcement learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Communication among agents and message-passing algorithms | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Auctions and mechanism design | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Swarm intelligence and collective behavior | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Multi-robot systems and coordination | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Multi-agent pathfinding and planning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Privacy and security in multi-agent systems | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Human-agent interaction and explainable AI | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Applications of multi-agent systems in smart cities | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 15 | Review | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 14 | 14 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||