Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules. - 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation. - 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
- instruction
- documentation
- examination
Natural, social, and engineered complex systems can be modelled as being composed of agents interacting with one another and their environment. This course introduces students to the theory, tools and techniques for understanding and solving problems related to such systems.
The course is composed of two parts. In the first one, both cooperative and selfish agents and interactions between them will be discussed. The methodological support will be provided by game theory.
In the second part, the focus will be on the study and analysis of models of systems in the aim of understanding the conditions under which certain properties can emerge and agent might learn certain strategies or behaviours by interacting with the environment and themselves.
Throughout the course, several application areas such as cooperation and competition, social influence and reinforcement learning will be discussed.
Prerequisites
Basic knowledge of probability, algebra, calculus and differential equations. Basics of procedural programming and ability to implement small programs in an arbitrary language, e.g. Python, Matlab, R, Java, C#, C++, C, etc.
Learning Objectives
A successful participant of this course is able to
- understand the rationale of multi-agent systems and their modelling.
- model scenarios with multiple interacting agents in the language of game theory
- evaluate the feasibility of achieving goals with agents using game theory
- understand the basic approaches to multi-agent learning, their peculiarities and their differences
- learn to choose the appropriate class of models with agents to characterise different complex systems
- implement in an efficient way a model of a system, then understand and analyse the corresponding outputs
Contents of Module
- Review of single-agent decision making and learning
- Multi-agent interaction:
- games in normal form, dominant strategies,
- Nash equilibria, Pareto optimality,
- partial observability,
- cooperative and coalition games, Shapley value,
- repeated games,
- Multi-agent learning:
- model based approaches: fictitious and rational learning
- model-free approaches: no regret and reinforcement learning
Teaching and Learning Methods
- Lectures
- Exercises and homework
- Practical work with appropriate tools
- Literature studies
Literature
- A Concise Introduction to Multi-Agent Systems and Distributed Artificial Intelligence. Nikos Vlassis. Morgan & Claypool Publishers, 2007.
- Introduction to Multi-Agent Systems - 2nd Edition. Michael Wooldridge. John Wiley & Sons, 2009.
- Multi-Agent Systems. Yoav Shoham and Kevin Leyton-Brown. Cambridge University Press, 2009.
- Artificial Intelligence, A Modern Approach (4th Edition). Stuart Russell and Peter Norvig. Pearson. 2021
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