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
Discovery Early Career Researcher Award - Grant ID: DE230101281
Funder
Australian Research Council
Funding Amount
$329,278.00
Summary
An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can ....An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can automatically find such solutions. The decision-makers can then choose the final solution based on their expertise and preferences. This expects to offer significant benefits to diverse engineering disciplines by finding superior and more practical solutions to their complex multidisciplinary problems.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.
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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.
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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.
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Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation syste ....Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation systems and novel approaches to continuous collaborative learning from multi-spectral media. In addition to the emerging partnership between participants, the project will advance sovereign capability to develop maritime intelligence gathering technology for the Royal Australian Navy to underpin stability in our region. Read moreRead less
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less