Skip to main navigationSkip to main content
The University of Southampton

COMP6207 Advanced Intelligent Agents

Module Overview

This module: - Introduces the students to the key issues of interaction of multiple self-interested parties (a.k.a. agents) and gives a broad survey of topics at the interface of theoretical computer science and game theory dealing with such interactions. - Provides the theoretical background and practical tools to solve problems arising in settings with self-interested participants, to predict possible behaviour and outcomes, and finally, to design multi-agent systems that would incentivise desirable behaviour. - Introduces the students to the specifics of computational game-theoretic techniques in different application areas, ranging from multi-agent systems, electronic marketplaces and networked computer systems to computational biology and social networks. - Extends and advances the knowledge obtained in other AI modules (in particular, COMP6203 Intelligent Agents).

Aims and Objectives

Module Aims

To provide an overview of advanced intelligent agents

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Sponsored search auctions and their properties
  • The principles of mechanism design and its use to shape incentives and designing markets
  • The main mechanisms including the second-price and Vickrey-Clarke-Groves auctions
  • Potential games and their application to congestion and routing problems
  • The main voting protocols
  • The issues of manipulation in voting
  • Cooperative games and coalition formation
  • The computational complexity of finding game theoretic solutions
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • As an element of an interactive system with multiple self-interested participants: to reason about the opponents' behaviour and make strategic decisions
  • As a system administrator: to analyse participant behaviours and predict likely outcomes
  • As a system designer: to create incentives for participants that result in systems with desirable properties, and encourage cooperation
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Find dominant strategy and min-max solutions, and compute pure strategy and mixed Nash equilibria in certain game classes
  • Apply learning techniques for finding stable outcomes
  • Derive parameters for producing optimal/efficient mechanisms in specific application domains
  • Apply cooperative solution concepts, such as the Core and the Shapley value, to analyse and design effective cooperative systems


Mechanism design without money - assignment problems - stable matching - social welfare maximisation, social cost minimisation, approximation ratio Mechanism design with money - revenue maximisation and revenue equivalence theorem - general mechanisms: formalism; revelation principle; desirable properties - Vickrey-Clarke-Groves mechanism - advanced mechanisms: combinatorial auctions; simultaneous auctions; sequential auctions; double auctions Learning in games - Bandit theory - Regret minimisation Applications - Network and routing games, (generalised) congestion games - Prediction markets

Learning and Teaching

Teaching and learning methods

Teaching methods include: - Directed reading and preperation: including, but not limited to, lecture notes, selected research papers and book chapters, as part of necessary preparation for the lectures. - Lectures: Three hours per week during the teaching weeks, supported by in-class quizzes and class discussion. - Tutorials: One hour per week during the teaching weeks, focusing on the application of the practical aspects of computational game-theoretic techniques and problem solving. - Assessment: There are a few Assignments. Learning activities include: - Work in groups: Reading and discussion groups with a plenary feedback during the tutorials. Participation, while not compulsory, is encouraged. - Individual study: Certain amount of (directed) reading is expected during your private study time. It is strongly recommended that you read around the subject areas covered in the lectures. Refer to the list of background texts and ask the lecturers if you require further resources. - Exercise: Applying theoretical knowledge and practical tools obtained in the lectures to solve example problems provided in the recommended literature and home take assignments.

Wider reading or practice45
Completion of assessment task11
Preparation for scheduled sessions18
Follow-up work18
Total study time150

Resources & Reading list

Y. Shoham, K. Leyton-Brown (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. 


Assessment Strategy

Feedback and student support during module study: - Assignments will be marked and feedback given during the tutorial sessions. - Reading and discussion groups with a plenary feedback during the tutorials. Relationship between the teaching, learning and assessment methods and the planned learning outcomes: - The knowledge, understanding and intellectual skills listed will be taught in lectures. In completing the assignments and examination you will demonstrate your mastery of all the skills listed. - The purpose of the tutorials is for you to master the skills and provide feedback on your understanding of topics not covered by, or are difficult to fully assess in, an assignment.


MethodPercentage contribution
Coursework 25%
Examination  (2 hours) 75%


MethodPercentage contribution
Examination 100%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: COMP6203

Share this module Share this on Facebook Share this on Twitter Share this on Weibo

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.