Building software modules that can learn from, and adapt to, a changing and unknown environment is a key challenge faced in many complex real-world problems. 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 swarm intelligence, contrasted against traditional optimisation techniques. The representations and algorithms explored in the unit can be used to solve problems ranging from complex optimisation to adaptive learning, 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 opportunities to explore the above-mentioned advanced topics in artificial intelligence and adaptive systems, research into a topic or technique of interest and develop and apply software solutions in simulated environments.

Students are able to (1) understand the general concepts and approaches used in building AI and adaptive systems; (2) perform a literature search and research investigation on at least one AI approach; (3) apply at least one AI approach to solve significant real-world problems; (4) participate effectively as a member of a team and contribute constructively to team goals; (5) produce scientific writing that explains the hypothesis, experimental design, and evaluation strategy of a problem solution ; and (6) explain AI approaches and their application in seminar settings..