Units / FIT5226
FIT5226 · Multi agent systems and collective behaviour
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
A multi-agent system (MAS) consists of a number of autonomous agents interacting with each other and with their environment. MAS is one of the fastest-growing areas of AI and a very general paradigm to understand many complex natural phenomena, such as the behaviour of ant colonies, fish swarms and human groups. Conversely, MAS approaches are crucial in the design of some of the most cutting-edge AI and cyber-physical systems, such as swarm robots. Hybrid cyber-physical systems, in which natural and artificial agents interact, represent the third important form of MAS. The internet, where millions of humans and computational agents interact in intricate and complex ways, is the paradigmatic example of such a hybrid. This highly interdisciplinary unit discusses the most important methods to describe, analyse and design MAS and discusses their practical applications in scientific modelling and artificial intelligence.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Suzhou (SEU) | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | In-semester Assessment a | Project | 15% | — |
| 2 | In-semester Assessment b | Project | 35% | — |
| 3 | Scheduled final assessment | Examination | 50% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
Learning outcomes
- Judge whether a particular real-world problem or application can usefully be modelled as a MAS
- Select MAS modelling methods suited to the problem
- Apply formal MAS modelling approaches as appropriate
- Implement and deploy MAS models to answer the relevant questions about a given real-world scenario
- Discuss the limitations of the modelling approaches and use multi-model solutions to mitigate or overcome these
Workload
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
| Activity | Duration |
|---|---|
| Seminars | 24 hours |
| Laboratories | 22 hours |
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