Explainable Artificial Creativity. This project aims to develop explainable models for creative AI systems which enable more productive and satisfying interactions between them and their human co-creators. This will boost both human and machine creativity through sustained, ongoing exchanges, leading to high-quality creative outcomes via automated ideation and more advanced human-machine collaborations. The proposed techniques will be validated with creative professionals, ensuring practical ind ....Explainable Artificial Creativity. This project aims to develop explainable models for creative AI systems which enable more productive and satisfying interactions between them and their human co-creators. This will boost both human and machine creativity through sustained, ongoing exchanges, leading to high-quality creative outcomes via automated ideation and more advanced human-machine collaborations. The proposed techniques will be validated with creative professionals, ensuring practical industry relevance. We expect the outcomes to include new methods that automatically generate persuasive explanations, new forms of communication including dialogues between creative AI systems and users, and new understanding of general aspects of explainability for creative AI systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Cybersecurity ethics training simulations for values-based decision-making. This Project will investigate ways to train reflective ethical decision making in cybersecurity management through the design of interactive social simulations. The Project will advance understanding and management of human factors in cybersecurity breaches and the field of serious game design for cybersecurity training by using new techniques for building artificially intelligent virtual agents, drawing on interdiscipli ....Cybersecurity ethics training simulations for values-based decision-making. This Project will investigate ways to train reflective ethical decision making in cybersecurity management through the design of interactive social simulations. The Project will advance understanding and management of human factors in cybersecurity breaches and the field of serious game design for cybersecurity training by using new techniques for building artificially intelligent virtual agents, drawing on interdisciplinary expertise in ethics, artificial intelligence and serious game design. Expected outcomes of the Project include a new framework and technologies for cybersecurity training. This should provide significant benefits through deeper understanding of the ethical impact of new cybertechnologies and training solutions.Read moreRead less
A pictorial communication framework for inclusion. A picture can tell a thousand words; many people with an intellectual disability can only communicate with pictures and are otherwise isolated. This project will research how computers can understand and facilitate a rich pictorial communication between people and person to machine, thereby supporting inclusion. This will be achieved by inclusively co-designing with people with ID and members of the community: a) an audio-visual accessible searc ....A pictorial communication framework for inclusion. A picture can tell a thousand words; many people with an intellectual disability can only communicate with pictures and are otherwise isolated. This project will research how computers can understand and facilitate a rich pictorial communication between people and person to machine, thereby supporting inclusion. This will be achieved by inclusively co-designing with people with ID and members of the community: a) an audio-visual accessible search tool, b) a pictorial communication device, and c) a visual inclusive social network. These applications will inform future innovations for everyone, and allow citizens with ID to access online information, participate in community activities and be included in the workplace.Read moreRead less
AI-Human Empowered Team Decision-Making. This project aims to introduce machine intelligence into human team decision-making using the brain-to-brain synchrony that arises when people cooperate toward achieving a goal. The expected outcomes are models and indicators of this synchrony, and methods to fuse individual human decisions with autonomous machine agents, into collective decisions. This new knowledge is expected to greatly increase our understanding of cooperative decision-making by human ....AI-Human Empowered Team Decision-Making. This project aims to introduce machine intelligence into human team decision-making using the brain-to-brain synchrony that arises when people cooperate toward achieving a goal. The expected outcomes are models and indicators of this synchrony, and methods to fuse individual human decisions with autonomous machine agents, into collective decisions. This new knowledge is expected to greatly increase our understanding of cooperative decision-making by humans and machine agents. The tools produced are expected to provide a computational basis for human-autonomy teaming, the core of Industry 5.0, that software developers and end-users in various industries could further build upon to optimise complex decision-making to benefit humanity.Read moreRead less
Adaptive modelling of human responses in complex interaction. This project aims to combine strengths of human cognition and evolutionary computing to efficiently solve problems which neither can do alone. The project will develop techniques combining advanced non-intrusive sensor measures of behaviour and emotional reaction in interaction tasks to enable high level computer support for human goal seeking, in complex data and design environments. This project will allow non-expert users to use to ....Adaptive modelling of human responses in complex interaction. This project aims to combine strengths of human cognition and evolutionary computing to efficiently solve problems which neither can do alone. The project will develop techniques combining advanced non-intrusive sensor measures of behaviour and emotional reaction in interaction tasks to enable high level computer support for human goal seeking, in complex data and design environments. This project will allow non-expert users to use tools normally requiring extensive training in settings where the user can 'see' when they get something they like but do not know how to instruct a computer system to show or do it. Applications of the project will include visualisation for bespoke manufacturing or for high dimensional data, generating abstract art, or improving teleconferencing systems.Read moreRead less
Brain Robot Interface for Physical Human Robot Collaboration. This project aims to discover new knowledge of cognitive conflict and develop models and algorithms that enable intuitive physical human-robot collaboration to jointly conduct laborious tasks in complex, unstructured environments. It proposes to build on responses in the human brain when a robot does not operate in a way the human expects. Conflict models and prediction method are planned using advanced machine learning algorithms. Th ....Brain Robot Interface for Physical Human Robot Collaboration. This project aims to discover new knowledge of cognitive conflict and develop models and algorithms that enable intuitive physical human-robot collaboration to jointly conduct laborious tasks in complex, unstructured environments. It proposes to build on responses in the human brain when a robot does not operate in a way the human expects. Conflict models and prediction method are planned using advanced machine learning algorithms. The model and algorithms are intended to be integrated into an innovative brain-robot interface for field testing in a real-world industrial task. Translation of the outcomes to industry is expected to produce substantial economic and societal benefits through improved productivity and safety.Read moreRead less