FIT3203 Intelligent agents
Faculty of Information Technology
FIT3203 Intelligent agents is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology. It isn't offered in 2026. It needs FIT2004 and FIT2111.
- Credit points
- 6
- Offered in 2026
- Not offered
- Assessment
- No exam
- 3 tasks
- Workload
- 144 hours
- per semester
This is the 2026 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3203
Prerequisites
Pass these before you enrol.
After FIT3203
No unit lists FIT3203 as a prerequisite in the 2026 handbook.
Overview
This unit introduces you to the field of Artificial Intelligence (AI) as a specialisation within Computer Science. It provides an overview of the foundations of AI, including the history, key concepts, and applications across diverse domains such as language, vision, and intelligent decision-making. You will explore introductory AI problem-solving techniques and develop a basic understanding of how AI systems are designed to emulate aspects of intelligence.
In parallel, the unit equips you with the essential mathematical and computational foundations required for later AI units. These include fundamental concepts from linear algebra and vector calculus, introduced in the context of solving simple AI-related problems such as classification and optimisation. Practical labs emphasise hands-on engagement through simulations, mathematical reasoning, and basic AI model-building, along with reflection on the societal and ethical implications of AI technologies.
Offerings in 2026
The 2026 handbook lists no offerings for FIT3203.
Assessment
- Assessment Task 1: Reinforcement Learning AgentArtefact30%
- Assessment Task 2: Multi-Agent PathfindingArtefact20%
- Assessment Task 3: Game Theory and Agent Decision-MakingWritten50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Explain the characteristics and architectures of intelligent agents, planning and automated reasoning.
- 2
Compare model-based and model-free reinforcement learning.
- 3
Construct intelligent agents to achieve defined goals efficiently
- 4
Build simulations under which emergent large-scale phenomena appear as a result of local agent-level interactions.
- 5
Evaluate the performance and limitations of intelligent agent systems in practical applications.
Workload and teaching
- Workshops24 hours
- Applied sessions22 hours
- Teaching approachActive learning
- Teaching approachEnquiry-based learning
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.
Where it fits
FIT3203 is part of 1 area of study in the 2026 handbook.
Common questions
When is FIT3203 offered?
FIT3203 has no offerings listed in the 2026 handbook.
How much work is FIT3203?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3203 have an exam?
No. FIT3203 has 3 assessment tasks and no exam.
Which majors and minors include FIT3203?
FIT3203 is part of Artificial intelligence.
More details
- Credit points
- 6
- Level
- 3
- Study level
- Undergraduate
- Faculty
- Faculty of Information Technology
- Type
- Coursework
- EFTSL
- 0.125
- Student contribution
- SCA Band 2
- Study abroad
- Available
- Handbook years
- 20262027