An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving futur ....An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving future secure key distribution. Multi-agent interactions in higher dimensional spaces are considered intractable using traditional matrix methods and this project will build on our exciting new breakthrough showing that such interactions are tractable using geometric multivectors.Read moreRead less
Shared-space interactions between people and autonomous vehicles. This project aims to understand how autonomous vehicles in urban environments need to interact with the people that they share those spaces with. Autonomous vehicles that are able to operate in shared spaces, such as campuses and pedestrian zones, promise to improve urban life. However, their uptake depends heavily on public acceptance as they operate in close proximity to people. The project investigates whether people are more l ....Shared-space interactions between people and autonomous vehicles. This project aims to understand how autonomous vehicles in urban environments need to interact with the people that they share those spaces with. Autonomous vehicles that are able to operate in shared spaces, such as campuses and pedestrian zones, promise to improve urban life. However, their uptake depends heavily on public acceptance as they operate in close proximity to people. The project investigates whether people are more likely to trust the technology and feel safe if they are able to understand how the system makes decisions and to directly influence its behaviour. Outcomes are expected to promote safe behaviour around urban robotic applications and accelerate the uptake of autonomous systems in Australia’s cities. Read moreRead less
Automating real-time feedback in virtual reality training through data mining. This project will use data mining techniques to develop a real-time feedback system that can be used in virtual reality training environments. This system will not only improve trainees' learning, it will also lead to more efficient use of virtual reality training in industries such as aviation, aerospace, mining, health and emergency services.
Quantized identification of feedback control systems. The theory of system identification with quantified data underpins frontier technologies that enable more efficient and sustainable telecommunications, automotive and biomedical industry. This project extends the fundamental framework of quantified system identification. The work will enhance Australia's international standing in the control field.
Decentralisation and robustness for practical control of complex systems. This project aims to develop the theory and tools to address the control of complex interconnected systems. There is currently an enormous disconnect in decentralised control between the celebrated theoretical advances and the concepts that are used for implementation, or even for computation. The project expects to isolate the key reasons for this disconnect and develop ways to address the control of complex interconnecte ....Decentralisation and robustness for practical control of complex systems. This project aims to develop the theory and tools to address the control of complex interconnected systems. There is currently an enormous disconnect in decentralised control between the celebrated theoretical advances and the concepts that are used for implementation, or even for computation. The project expects to isolate the key reasons for this disconnect and develop ways to address the control of complex interconnected systems. The expected outcome is a tool which can observe information from only a small portion of a network but which may ultimately effect a large portion of the network. This includes smart building management, multi-vehicle systems and convoys, irrigation networks, large array telescopes, and the power distribution grid.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102601
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Distribution-free system identification: building models from experimental data under minimal statistical assumptions. In fields with strict safety or quality requirements, such as production control, communication and navigation, there is a great need for methods that can build models with guaranteed performance. However, there is a lack of efficient solutions that can work under minimal assumptions on the disturbances; the project aims at developing such methods.
Energy efficient sensing, computing and communication. This research will study trade-offs in resource use: bandwidth, power, and computational capacity of systems of sensors such as cameras, radars, and distributed sensor networks based on a statistical mechanical theory of information processing, leading to practical algorithms to optimize resource use in the design of such systems.
Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less
Optimal control with decentralised information. This project will deliver optimisation-based tools to underpin systematic engineering approaches to the management of complex and networked systems arising in diverse areas. Optimal control for achieving guaranteed behaviour finds application in transport, resource management and distribution, telecommunications, and robotics and automation.
Certified evaluation of uncertainty in models of dynamical systems. The purpose of this project is to develop methods which will aid engineers to better analyse the accuracy of models created using experimental data. To support the use of the methods, a toolbox with software implementations will also be developed.