UWA Handbook 2017

Unit details

CITS4404 Artificial Intelligence and Adaptive Systems

Credit 6 points
  Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Honours option in Computer Science and Software Engineering [Bachelor of Science (Honours)]
Content 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.
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 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.
Assessment Typically this unit is assessed in the following ways: (1) seminars; (2) a project; and (3) a 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 Lyndon While
Unit rules
Prerequisites: enrolment in the Master of Professional Engineering or Master of Data Science or Honours in Computer Science and Software Engineering; for pre-2012 courses: enrolment in honours or a higher degree by coursework in Computer Science and Software Engineering
Advisable prior study: CITS1001 Object-oriented Programming and Software Engineering and (CITS1002 Programming and Systems or CITS2002 Programming and Systems)
Incompatibility: CITS7212 Computational Intelligence
Unit Outlinehttp://www.unitoutlines.ecm.uwa.edu.au/Units/CITS4404/SEM-2/2017

  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
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