Multi-agent systems

Course goals

After completing the course, the student

• knows the main concepts and abstractions that are used in multi-agent systems (e.g., autonomous agents, environment, interaction, knowledge, preference, strategy, protocol, norms, responsibility)
• is acquainted with the theoretical aspects and problems of multi-agent systems (e.g., preference modelling, interaction analysis, communication, coordination, compliance check, responsibility assignment)
• knows basic concepts from decision and game theory (e.g., decision rules, various equilibria, mechanism design, social choice functions, voting mechanisms)
• is able to work with game theoretic concepts (e.g., determining different equilibria, working with different game settings such as strategic and extensive games, determining winners of various voting mechanism and auctions)
• is able to implement multiagent concepts such as strategy update rules and opponent modelling in game theoretic repeated interactions and analyse outcomes in terms of equilibria
• recognizes different applications of multi-agent systems (e.g., auctions, voting systems, social interactions)
• has insight into the strengths and weaknesses of multi-agent systems (e.g., distributed nature of multiagent systems, impossibility results from game theory and its relevance for multi-agent systems)
• recognizes the interdisciplinary nature of multi-agent systems (e.g., the relations between multi-agent systems with social sciences, psychology, and economy)

Assessment
The assessment consists of

• a written exam (70% of the final mark)
• a group assignment (30%).

The group assignment consists of 2 written reports and a Python implementation of agents using fixed or adaptive strategies in different game theoretic models.

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Content

This course provides an introduction to the field of multiagent systems.

The lectures cover the following topics:

• decision theory
• game theory
• communication
• social choice
• mechanism design
• auctionshttp://www.cs.uu.nl/docs/vakken/mas/2022/

The assignments cover multiagent system issues such as:

• repeated interactions
• strategy update rules
• opponent modelling
• decision-making under uncertainty
• development of multiagent Systems
• equilibria and outcome analysis

Course form
Lectures, tutorials, lab sessions, group assignments.
The tutorial and lab sessions aim at giving students experience in engineering multiagent systems. These sessions are organized around a student group assignment to design and develop a multiagent system.

For further details see the course webpage http://www.cs.uu.nl/docs/vakken/mas/2022/

Literature
Some sections of "Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundation", by Yoav Shoham and Kevin Leyton-Brown, Cambridge University Press, 2009.
This book is freely available on http://www.masfoundations.org/.