Quantitative psychological theories for a dynamic world. . The dynamic world around us means we need to constantly adjust our decisions in light of ever-changing influences, both external (weather, traffic ...) and internal (fatigue, learning ...). This project aims to understand how these changes affect performance. This will have significance for basic science, and also practical benefits for applied psychology. This project will examine the dynamic nature of psychological processes in a range ....Quantitative psychological theories for a dynamic world. . The dynamic world around us means we need to constantly adjust our decisions in light of ever-changing influences, both external (weather, traffic ...) and internal (fatigue, learning ...). This project aims to understand how these changes affect performance. This will have significance for basic science, and also practical benefits for applied psychology. This project will examine the dynamic nature of psychological processes in a range of settings: simple decisions, consumer decisions, human-machine interactions, and team performance. Theory development will lead to improved understanding of underlying cognitive processes, and transforms the measurement of decisions, which is important for applied psychological investigations. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101310
Funder
Australian Research Council
Funding Amount
$426,918.00
Summary
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the ....Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.Read moreRead less