CITS4404 Artificial Intelligence and Adaptive Systems
- 6 points
If this unit does not have an online alternative, then students who are presently unable to enter Western Australia and whose studies would be delayed by an inability to complete this unit, should contact the unit coordinator (details given on this page) to ascertain, on an individual case-by-case basis, if alternate arrangements can be made to support their study in this unit.
Availability Location Mode Not available in 2021 UWA (Perth) Face to face Predominantly face-to-face. On campus attendance required to complete this unit. May have accompanying resources online.
- 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 behaviours, 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 technique of interest and develop and apply software solutions in simulated environments.
- 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 and evaluation strategy, and 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 and, 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 areas 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 open problems and research questions in the research field of artificial intelligence and adaptive systems.
- Indicative assessments in this unit are as follows: (1) seminars; (2) project; and (3) final examination. Further information is available in the unit outline.
Supplementary assessment is not available in this unit except in the case of a bachelor's pass degree student who has obtained a mark of 45 to 49 overall and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
- Unit Coordinator(s)
- Dr Yuliya Karpievitch
- Unit rules
- enrolment in the
MJD-ARTIF Artificial Intelligence or
HON-CMSSE Computer Science and Software Engineering or
62510 Master of Information Technology or
62530 Master of Data Science or
62550 Master of Professional Engineering (Electrical and Electronic Engineering or Software Engineering)
completion of 12 points of programming-based units
- CITS7212 Computational Intelligence
- This unit will not be offered in 2021.
- The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
- All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
- Unit readings, including any essential textbooks, are listed in the unit outline for each unit, one week prior the commencement of study. The unit outline will be available via the LMS and the UWA Handbook one week prior the commencement of study. Reading lists and essential textbooks are subject to change each semester. Information on essential textbooks will also be made available on the Essential Textbooks. This website is updated regularly in the lead up to semester so content may change. It is recommended that students purchase essential textbooks for convenience due to the frequency with which they will be required during the unit. A limited number of textbooks will be made available from the Library in print and will also be made available online wherever possible. Essential textbooks can be purchased from the commercial vendors to secure the best deal. The Student Guild can provide assistance on where to purchase books if required. Books can be purchased second hand at the Guild Secondhand bookshop (second floor, Guild Village), which is located on campus.