The University of Western Australia
School of Computer Science and Software Engineering

School of Computer Science and Software Engineering

CITS4404 Artificial Intelligence & Adaptive Systems Home


Building software modules that can learn from and adapt to a changing and unknown environment is the challenge facing many real-world problems, such as multi-robot coordination and navigation, modelling and problem solving for large complex systems. This unit covers a class of nature inspired algorithms and structures for creating programs that demonstrate emergent adaptive and intelligent behavious, including evolutionary algorithms, neural networks, machine learning and a range of optimisation techniques powered by swarm intelligence. They can be used to solve problems ranging from complex optimisation, adaptive learning to knowledge acquisition, which form the core research areas of artificial intelligence. Numerous research questions remain when such techniques are applied in real-world situations. In this interactive, project-based unit, students are given the opportunities to explore the above-mentioned advanced topics in artificial intelligence and adaptive systems, research into one topic or techinque of interest, develop and apply software solutions in simulated environments.

Outcomes: Students are able to

  1. explain research questions, proposed solutions and evaluation techniques to peers and research groups in seminar settings effectively using oral communication;
  2. produce scientific writing such as research papers that explain the hypothesis, experimental design, evaluation strategy and are able to sythesise and draw comparison with existing solutions;
  3. locate, digest, use and reference relevant information in the area of artificial intelligence and adaptive systems;
  4. participate effectively as a member of a team, in particular, value alternative and diverse viewpoints, and contribute constructively to the overall team goal;
  5. discuss the general concepts and approaches taken for building adaptive systems;
  6. carry out focused research investigation and literature search on one particular approach of interest;
  7. describe the important underlying technologies in artificial intelligence and adaptive systems—neural networks, evolutionary algorithms, machine learning and various nature inspired optimisation techniques;
  8. develop special expertise in one of the above area of research, appreciate the fundamentals of the area, and understand the current trend and the state of the art;
  9. apply the techniques of selection to solve unseen/undocumented problems;
  10. develop competency in formulating problems, devise computation models, build algorithms and software modules to solve problems that requires intelligent and adaptive solutions;
  11. appreciate the role of artificial intelligence and adaptive sytems in real-world problem solving and complex system modelling; and
  12. critically discuss on open problems and research questions in the research field of artificial intelligence and adaptive systems.

Unit coordinator: Dr. Lyndon While
Lecture: Tuesdays 8-10am in GP2.
Consultation: Tuesdays 10-11am in CSSE Rm 1.14.
Only the first three lectures will be recorded and will be available via LMS.


The assessment for CITS4404 consists of seminars, a project, and an exam.

Assessment Value Assessment Dates
Seminars 20% Weeks 5-8
Project 40% Available: Week 3; due: end of Week 13
Exam 40% November exam period

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