Module Details for the Academic Year 2018/2019

COMP47410 Computational Creativity

Computational Creativity (CC) is a new branch of Artificial Intelligence and
Cognitive Science that explores the potential of machines to perform tasks in ways
that would be considered creative if performed by a human, or to generate outputs
that would be considered novel and interesting if generated by a human. As a field,
CC focuses primarily on the latter, to explore the generative potential of machines
and to focus on the building of software systems that construct original artifacts
(whether linguistic – as in stories, poems, jokes, slogans, tweets, etc. – or visual –
such as collages, paintings, patterns etc. – or musical – such as jazz riffs, pieces of
classical music, etc.)
Given its focus on creativity in humans and machines, a course on CC necessarily
mixes elements of cognitive science, psychology and philosophy into its core
computational structure. The course should appeal to students with an interest in
creativity, or an interest in AI that is not served by courses that emphasis problem-
solving. The course will be delivered through lectures and practical sessions, and will
require students to build generative systems of their own. Weekly assignments will
dovetail with the course project, so that work initiated in the assignments (and
incrementally build upon each week) will be completed in the project.
Lectures will involve instruction, discussion and debate about the nature of creativity,
the potential of machines to be creative, and the practicalities of building creative

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On successful completion of this module the learner will be able to:
1. Understand the relevant concepts in the philosophy of AI, psychology and
computer science as their pertain to human and machine creativity
2. Understand how the study of machines can inform our understanding of
human cognition, and vice versa, with relation to dominant theories
3. Build their own generative systems in a programming language like Java
(e.g. as in the construction of an automated Twitterbot)
4. Know how to access and re-use existing Creative systems on the Web
4. Understand how to evaluate generative/creative systems empirically 
Item Workload




Autonomous Student Learning




Description % of Final Grade Timing
Continuous Assessment: Assignment 1


Continuous Assessment: Assignment 2


Continuous Assessment: Assignment 3


Continuous Assessment: Assignment 4


Examination: < Description >


2 hour End of Semester Exam
Project: Started via assignments 1 thru 4




This module is not passable by compensation

Resit Opportunities

In-semester assessment


If you fail this module you may repeat the course; if you fail the written exam only you may resit theexam.

Module Requisites and Incompatibles

Pre-Requisite: Software Engineering Project 3 (COMP30050)

Equivalent Modules

Prior Learning

Artificial Intelligence (though not necessary)
Curricular information is subject to change