Professor Maurice Pagnucco
Deputy Dean (Education)
Dean's Unit, Level 6, Building K17,
Faculty of Engineering,
University of New South Wales,
NSW, 2052, AUSTRALIA
Telephone: +61 2 9385 5000
Maurice Pagnucco Research Projects
I am particularly interested in logic-based approaches to artificial intelligence and their applications, particularly to robotics. Here is a list of suggested topic areas. Also, if you're interested in these areas and would like to propose or discuss an alternative project, please contact me.
The following projects can be offered at PhD, Masters by Research, Postgraduate Coursework Masters research project or Honours/Thesis level. I have grouped them into several main themes:
- Ethics and AI
- Symbolic and Sub-Symbolic AI
- Automated Disassembly of End-of-Life Products
- Knowledge Representation and Reasoning
- RoboCup Soccer and @Home
Ethics and AI
Representing and Reasoning with Ethical Principles in AI Programs
Current advances in artificial intelligence (AI) have led to a great interest in ensuring that AI programs adhere to certain ethical principles or behavioural norms. This project will explore an approach to this issue using the experimental cognitive robotic programming language Golog. It will be implemented on one of the robot platforms in the CSE robotics lab: Softbank Nao or Toyota HSR.
Symbolic and Sub-Symbolic AI
Integrating Symbolic and Sub-Symbolic Artificial Intelligence
With Dr Yang Song.
Over recent years, machine and deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases purely data-driven approaches would provide suboptimal results especially when not enough high-quality data are available for training the models. Moreover, we hypothesise that prior-knowledge, such as logic rules and knowledge graphs, if integrated with the learning-based models can provide improved performance with a smaller amount of training data.
In this PhD project, we will investigate novel methods that effectively integrate knowledge representation and reasoning with machine/deep learning, which are the symbolic and subsymbolic approaches of artificial intelligence. We will also investigate the novel applications of the designed methods in various domains, such as computer vision, human-robot interaction and autonomous driving.
Explainable AI (XAI)
With Dr Yang Song.
In the last few years, AI and in particular deep learning have achieved remarkable success in many applications. However, without symbolic representation, learning-based systems cause difficulties in understanding their working principles and devising ways for optimisation, and raise questions around trust and fairness. Explainable AI (XAI) has thus become an emerging field of research, which aims to create more explainable models that enable humans to understand, trust and manage the systems. This is especially important for critical applications such as autonomous driving and healthcare. In this project, we will investigate various ways of developing XAI, such as visual explanation, explanations by example and explanations by simplification. Novel methodologies will be developed and integrated with current learning-based systems. Experimental evaluation will be conducted on various application domains such as human-robot interaction and biomedical image analysis.
Deep Learning in Social and Interactive Environments
With Dr Yang Song.
Visual information from images and videos can provide important social signals, such as social relationship, emotion and intention, which affect the downstream tasks such as movement and behaviour prediction. Predicting human movement, for instance, enables robots to conduct in a socially acceptable manner. This new research area, artificial social intelligence, covers a wide range of research topics that typically require semantic analysis, graphical modelling, video analysis and generative models, which are key areas in computer vision and deep learning. In this project, we will focus on a particular research topic in artificial social intelligence and develop novel deep learning based approaches. Example topics include path prediction, social relationship recognition, action recognition, behaviour prediction and human-object interaction. Addressing these problems is a crucial step for many autonomous platforms such as self-driving cars, social robots and surveillance.
Automated Disassembly of End-of-Life Products
A Generic and Robust Ontology and Knowledge-Base System for Robotic Disassembly of E-Waste
Current practises in treatment of hazardous end-of-life products such as electronic waste (or e-waste) include disposal in landfill, shredding for recycling or manual disassembly. They come with a variety of issues such as having detrimental effects on the environment, lacking safety and ethical regulations in developing countries, or failing to efficiently utilise remaining energy and resources embodied within the product. A cognitive robotic disassembly system which addresses these issues whilst being able to handle uncertainties prevalent in end-of-life products requires an ontology or other means of storing knowledge and relationships to reason about. This project will involve developing, testing and evaluating a generic but robust ontology and knowledge-base system of products and their components, fasteners, joining methods, disassembly actions and tools.
Machine Learning Computer Vision for Robotic Disassembly of E-Waste
Current practises in treatment of hazardous end-of-life products such as electronic waste (or e-waste) include disposal in landfill, shredding for recycling or manual disassembly. They come with a variety of issues such as having detrimental effects on the environment, lack safety and ethical regulations in developing countries, or fail to efficiently utilise remaining energy and resources embodied within the product. A cognitive robotic disassembly system which addresses these issues whilst being able to handle uncertainties prevalent in end-of-life products requires a computer vision system capable of correctly identifying various components and fasteners such as screws. This project will involve developing a substantial enough dataset of images for training and applying machine learning computer vision techniques for screw detection. Various machine learning algorithms will need to be explored to select the most appropriate methods.
Knowledge Represenatation and Reasoning
Building a Meta Learning Management System
There have been many Learning Management Systems (LMS) developed for providing students with course materials, guidance through a course, etc. Examples include Moodle (which is used by UNSW), WebCMS developed in CSE, OpenLearing (developed in CSE), Smart Sparrow (developed in CSE), Canvas, etc. One issue that is not completely addressed by these systems is the ability to easily repurpose and reuse digital assets and content. Most provide a way of using course content from a previous offering of a course however this only provides a limited amount of flexibility. This project proposed building a Meta LMS. That is a learning management system that focus on a more fine-grained view of education resources, digital assets, contents, etc. and provides the ability to select from these resources to populate an LMS.
RoboCup Soccer and @Home
UNSW's rUNSWift RoboCup soccer team and sUNSWeep RoboCup@Home team compete in the annual RoboCup international robotics competition. I can propose several projects working with these teams.
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