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
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
Discovery Early Career Researcher Award - Grant ID: DE200100898
Funder
Australian Research Council
Funding Amount
$421,979.00
Summary
Information Embodiment Framework for Education using Immersive Technologies. The project aims to develop a framework to apply mixed reality technologies in education, by fusing the information with human physical, physiological, cognitive and emotional perceptions. The current approach translates existing contents into 3D which does not scale and causes cognitive overload. The project conceptually advances the design and development of mixed reality applications. The expected outcome is a mixed ....Information Embodiment Framework for Education using Immersive Technologies. The project aims to develop a framework to apply mixed reality technologies in education, by fusing the information with human physical, physiological, cognitive and emotional perceptions. The current approach translates existing contents into 3D which does not scale and causes cognitive overload. The project conceptually advances the design and development of mixed reality applications. The expected outcome is a mixed reality integration framework for effective communication, applicable to manufacturing, health, tourism and arts. The benefits are enhanced learning with engaging resources, with positive impact on student learning outcomes and motivation, in both formal and informal education settings such as schools, galleries, and museums.Read moreRead less
Human-Machine Teaming:Designing synergistic learning of humans and machines. This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-de ....Human-Machine Teaming:Designing synergistic learning of humans and machines. This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-design and iterative prototyping in the domains of radiology training and environmental learning, we will devise and evaluate exemplar systems that support humans to interactively frame problems, explore and learn, while utilising and improving machine models, leading to a guiding framework for designing human-machine teaming.Read moreRead less
Accessible Data Exploration and Analysis for Blind People. This project aims to develop new assistive technologies that will enable blind people to explore and analyse data more readily. The project expects to generate new knowledge in the fields of assistive technology, multimodal interfaces, dialogue systems and natural language understanding and generation. The expected outcome of the project is an innovative conversational agent that uses a mix of speech and tactile graphics to communicate ....Accessible Data Exploration and Analysis for Blind People. This project aims to develop new assistive technologies that will enable blind people to explore and analyse data more readily. The project expects to generate new knowledge in the fields of assistive technology, multimodal interfaces, dialogue systems and natural language understanding and generation. The expected outcome of the project is an innovative conversational agent that uses a mix of speech and tactile graphics to communicate with a blind user and proactively assists with data analysis tasks. This should provide significant benefits, as it will overcome barriers to data analysis and exploration by blind people that currently restrict access to government, health and personal data, and limit employment opportunities.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
A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Exp ....A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Expected outcomes include the discovery of an acceptable, transparent and ethical explanation system that helps humans to understand the AVs decision making. This field will continue to rise in prominence and produce much-needed work to improve the widespread adoption of AVs.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100315
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Social attentive user interfaces for the age of interruption. This proposal aims to enable the development of social attentive user interfaces—those that employ sensors such as eye trackers and thermal cameras to monitor the locus and level of users' attention and adapt their behaviour accordingly. The project lies in the field of Human-Computer Interaction, drawing from machine learning methods to design novel user experiences. Expected outcomes include insights into how people manage their att ....Social attentive user interfaces for the age of interruption. This proposal aims to enable the development of social attentive user interfaces—those that employ sensors such as eye trackers and thermal cameras to monitor the locus and level of users' attention and adapt their behaviour accordingly. The project lies in the field of Human-Computer Interaction, drawing from machine learning methods to design novel user experiences. Expected outcomes include insights into how people manage their attention, new methods for attention estimation and classification, and novel systems for e-learning and work productivity that demonstrate these new capabilities. As a result, this project will provide the benefit of enabling system to no longer be blind to users’ attentional, social, and cognitive contexts.Read moreRead less
Advancing Australia’s hospitality industry through interactive food. This project aims to develop the first framework for the design of interactive food to advance Australia’s hospitality industry. The project expects to co-develop with restaurateurs and chefs interactive sounds, smells and tastes technologies that enable them to create novel eating out experiences and evaluate diners’ reactions. The expected outcome is an easy-to-use toolkit (comprising a software suite and low-cost sensors) th ....Advancing Australia’s hospitality industry through interactive food. This project aims to develop the first framework for the design of interactive food to advance Australia’s hospitality industry. The project expects to co-develop with restaurateurs and chefs interactive sounds, smells and tastes technologies that enable them to create novel eating out experiences and evaluate diners’ reactions. The expected outcome is an easy-to-use toolkit (comprising a software suite and low-cost sensors) that can be readily incorporated into hospitality operations. This should provide significant benefits, such as enticing people to go out and visit restaurants, supporting some of Australia’s 600,000 hospitality jobs while fostering Australia’s innovative food culture.Read moreRead less