ACS332 Agent-based modelling and multi-agent systems

Module Description

This module introduces multi-agent systems and agent-based modelling. It will motivate the many diverse types of complex adaptive system that can be represented and simulated by these approaches (including engineering applications, artificial intelligence and socio-technical systems), and establish a typology of phenomena that typically indicate that such approaches would be useful. We will introduce a set of key theories, methods and tools in the domain of multi-agent systems and agent-based models. Students will gain experience of implementing agent-based simulations in state-of-the-art software, building on their experience in C and Java programming. We will also investigate methods for how agent-based models can be tested and validated using empirical data.

Credits: 10 (Autumn Semester)

Module Leader

RPurshouseDr Robin Purshouse

Amy Johnson Building

If you have any questions about the module please talk to us during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact me via email, or drop in to see me.

Learning Outcomes

Learning Outcomes

By the end of the module students will be able to:

  • Explain how key themes in complex adaptive systems (such as emergence and adaptation) can be represented using agent-based approaches [SM1p, ET2p].
  • Describe and evaluate the decision-making architectures available for agent-
    based simulations and multi-agent systems [EA1p, EA2p].
  • Design small-scale multi-agent systems using UML methods. [D2p, D4p]
  • Implement a multi-agent simulation using open-source software tools. [EP1p,
    EP2p, EP3p, EP4p]
  • Calibrate an agent-based model to empirical data using a Bayesian framework
    (ACS6332 only). [SM1fl, SM2fl, EA3fl]

This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.



This module will include the following topics:

  • Concepts and phenomena in complex systems
  • Simple agent cognition (rules and folk psychology)
  • Complex agent cognition (artificial intelligence planning)
  • Simple network architectures (loops, lattices)
  • Complex network architectures (network characteristics, small-world networks, social circles)
  • Network dynamics (with a focus on stochastic actor based models)
  • Calibration and validation of agent-based models (approximate Bayesian computation, surrogate models) – ACS6332 onl
  • Agent-based modelling software platforms (with a focus on Repast)
  • Practical agent-based modelling (agile approaches, source code control, verification)
  • Applications of agent-based modelling and multi-agent systems

Teaching Methods

Learning and Teaching Methods

The main teaching methods are lectures, example and case-study classes, tutorial sessions as well as lab-based sessions (practical as well as computer-based labs). All learning outcomes will be addressed via the above teaching methods, with the addition of the use of industry-standard software in the form of instructor-led pc-lab sessions.

Lectures: 18 hours
Tutorials: 2 hours
Labs: 4 hours
Independent Study: 74 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available on MOLE.



Formal 2-hour exam 60%

Coursework 40%:



  • Feedback will be provided throughout the semester, via the scheduled laboratory/tutorial sessions.
Student Evaluation

Student Evaluation

Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of each semester.

Recommended Reading

Recommended Reading

Core Texts:

There is no core text for ACS332/6332. A sequence of recommended journal paper readings will be made available during the module.