Behaviour Bootstrapping for Ad Hoc, Heterogeneous Robot Swarms. This project aims to develop algorithms to permit groups of robots to evolve coordinated, collective, swarm behaviours. Groups of robots will be conceptualised as developmental swarm organisms with an initially limited set of behaviours, but equipped with structures and processes to permit them to evolve new behaviours. This project expects to deliver the next generation of computational intelligence technologies to enable humans to ....Behaviour Bootstrapping for Ad Hoc, Heterogeneous Robot Swarms. This project aims to develop algorithms to permit groups of robots to evolve coordinated, collective, swarm behaviours. Groups of robots will be conceptualised as developmental swarm organisms with an initially limited set of behaviours, but equipped with structures and processes to permit them to evolve new behaviours. This project expects to deliver the next generation of computational intelligence technologies to enable humans to harness large groups of robots for new kinds of transport and inspection tasks in smart cities, smart farming and defence. The expected outcomes of the project include new software frameworks for distributed developmental learning, extending developmental robotics to evolutionary robot swarms. Read moreRead less
Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, commun ....Machine education for trusted multi-skilled evolutionary learners . Transforming data assets into organisational knowledge assets sits in the hands of a few, highly specialised, data scientists. The aim of this research is to design educational instruments to support non-experts to teach artificial intelligence (AI) systems in a similar way to educating human teachers to teach human learners. The significance of the project lies in affording the wider smart, but not necessarily AI expert, community the ability to contribute to growing our knowledge-based society in a safe, transparent and trustworthy manner. Outcomes will include innovative instruments to teach machines, novel knowledge creation, trusted and transparent AI systems, and a new generation of human teachers specialised in educating AI systems.Read moreRead less
Brain mechanisms for coordinating with others through sound. Distinguishing between sounds produced by self and others is critical for interpersonal coordination and communication through speech and music. This project employs a novel dual-brain electrophysiological technique with tagged audio signals to elucidate how the human brain achieves this distinction, and when and why it cannot. Expected outcomes include new knowledge on the neurophysiological mechanisms that support self-other processi ....Brain mechanisms for coordinating with others through sound. Distinguishing between sounds produced by self and others is critical for interpersonal coordination and communication through speech and music. This project employs a novel dual-brain electrophysiological technique with tagged audio signals to elucidate how the human brain achieves this distinction, and when and why it cannot. Expected outcomes include new knowledge on the neurophysiological mechanisms that support self-other processing, and the acoustic conditions and behavioural strategies that facilitate their operation. These outcomes should ultimately have applied benefits for improving interpersonal coordination and social interaction, especially in digital environments and clinical populations with atypical self-other processing.Read moreRead less
Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision ....Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision making process. By doing so, we will develop a world-leading model of how the brain makes decisions, and also provide the broader scientific community with a set of exciting new tools for studying information processing in the brain.Read moreRead less
Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental ....Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental policy formulation, network design, engineering, defence and cybersecurity; offering significant benefits to the researchers and practitioners in these fields. In addition to research outputs, it will strengthen international collaboration and build research capacity to put Australia at the forefront of this research.
Read moreRead less
Robust evolutionary analytics for changing and uncertain environments. This project aims to develop a novel framework for solving planning problems in dynamic environments with uncertainties. Current methods treat these conditions as two discrete problems. In the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. The intended outcomes include a novel framework with new techniques, developed by ....Robust evolutionary analytics for changing and uncertain environments. This project aims to develop a novel framework for solving planning problems in dynamic environments with uncertainties. Current methods treat these conditions as two discrete problems. In the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. The intended outcomes include a novel framework with new techniques, developed by exploiting the assumptions of existing methodologies. Practical outcomes will include a robust planning tool.Read moreRead less
Evolutionary Framework for High Dimensional Problems. The project aims to develop a novel framework for solving high dimensional decision problems with and without changes. This research is driven by the fact, that there is a huge gap between current research and the methodology needed to solve practical decision problems. In the proposed framework, a number of algorithms will be developed and integrated to generate robust solutions for those problems. The intended scientific outcomes include a ....Evolutionary Framework for High Dimensional Problems. The project aims to develop a novel framework for solving high dimensional decision problems with and without changes. This research is driven by the fact, that there is a huge gap between current research and the methodology needed to solve practical decision problems. In the proposed framework, a number of algorithms will be developed and integrated to generate robust solutions for those problems. The intended scientific outcomes include a novel framework with new techniques, developed by exploiting the impractical assumptions of existing methodologies. Practical outcomes include a robust decision-making tool and strong research training. The developed tool will provide significant cost savings through better decision making in practice.Read moreRead less
Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a numb ....Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a number of fundamental
shortcomings which restrict their use in real applications. This project aims to investigate and overcome the underlying key challenges to advance knowledge and contribute towards diverse domains such as energy, transport and space research, helping deliver high quality robust designs.
Read moreRead less
Early career teacher induction: Supporting precarious teachers. This project aims to investigate the ways in which Australian induction policies support precariously employed early career teachers to effectively manage student classroom behaviour. This project expects to generate new knowledge of workforce development and induction experiences of early career teachers employed on casual and short-term contracts. Expected outcomes of this project include alternative policy and practice recommenda ....Early career teacher induction: Supporting precarious teachers. This project aims to investigate the ways in which Australian induction policies support precariously employed early career teachers to effectively manage student classroom behaviour. This project expects to generate new knowledge of workforce development and induction experiences of early career teachers employed on casual and short-term contracts. Expected outcomes of this project include alternative policy and practice recommendations to support the transition of insecure replacement teachers within the profession. The benefits of this research include, improving teachers’ classroom management practices; the retention of new teachers; improving teacher workforce development; and building a healthier education system. Read moreRead less
Estimating the Topology of Low-Dimensional Data Using Deep Neural Networks. This project will expand on the superhuman visual capabilities of deep neural networks to allow us to analyse the topology of 3- and 4-dimensional manifolds. While these spaces still count as low-dimensional, 4-dimensional manifolds typically are beyond human visual comprehension. The topology of a manifold describes its essential properties such as the number of connected components, holes, tunnels and cavities of vario ....Estimating the Topology of Low-Dimensional Data Using Deep Neural Networks. This project will expand on the superhuman visual capabilities of deep neural networks to allow us to analyse the topology of 3- and 4-dimensional manifolds. While these spaces still count as low-dimensional, 4-dimensional manifolds typically are beyond human visual comprehension. The topology of a manifold describes its essential properties such as the number of connected components, holes, tunnels and cavities of various dimensions. Traditional methods from computational topology fail in large practical applications due to computational restrictions. We propose an approximation that overcomes previous limitations and can open new doors to data analysis in material science, medical imaging, dynamical systems and other applications.
Read moreRead less