ACS6132 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). The module introduces 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 object-oriented programming and agile software development methods.

Credits: 15 (Autumn Semester)

Module Leader

RPurshouse














Professor Robin Purshouse

Email: r.purshouse@sheffield.ac.uk
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 [SM1m, SM1fl, ET2m, ET2fl].
  • Describe and evaluate the decision-making architectures available for agent-based simulations and multi-agent systems [EA2m,EA3fl].
  • Design small-scale multi-agent systems using UML methods. [D2m, D2fl, D4m]
  • Implement a multi-agent simulation using open-source software tools. [EP1m, EP1fl, EP2m, EP3m, EP4m, EP6m]
  • Calibrate an agent-based model to empirical data using a Bayesian framework [SM1m, SM1fl, SM2m, SM2fl, EA3m, EA3fl, EP8m]
  • Apply agile project management processes to a software development project [ET3m, ET3fl, EA4m]

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.

Syllabus

Syllabus

  • 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)
  • 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

NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.

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: 20 hours
Labs: 20 hours
Tutorials: 4 hours
Independent Study: 106 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available on Blackboard (MOLE).

Assessment

Assessment

100% coursework - group project

Feedback

Feedback

Feedback will be provided during discussions in the tutorial sessions and labs, and via the marks and feedback on the coursework elements.

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.

You can view the latest Department response to the survey feedback here.

Recommended Reading

Recommended Reading

Core Texts: