Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection ....Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection of anomalies. The established theories and developed algorithms will advance frontier technologies in machine intelligence. The success of the project will contribute to a wide range of real applications in cybersecurity, defence and finance, bringing massive social and economic benefits. Read moreRead less
Cost-effective Edge Service Provisioning in the Last Mile of 5G. This project aims to deliver a suite of novel approaches for enabling cost-effective last-mile service provisioning in the 5G mobile edge computing (MEC). This project is the world's first attempt to systematically tackle the critical service provisioning challenges in the last mile where base stations link users to MEC applications. It offers a practical solution for provisioning software vendors' MEC services cost-effectively. Th ....Cost-effective Edge Service Provisioning in the Last Mile of 5G. This project aims to deliver a suite of novel approaches for enabling cost-effective last-mile service provisioning in the 5G mobile edge computing (MEC). This project is the world's first attempt to systematically tackle the critical service provisioning challenges in the last mile where base stations link users to MEC applications. It offers a practical solution for provisioning software vendors' MEC services cost-effectively. This project should drive Australia's 5G transition and innovations, promote its post-COVID economic recovery and resilience by enabling various real-time mobile and IoT applications, e.g., telehealth, remote learning/working, industry 4.0, and ensure its pioneering position in the global 5G research.Read moreRead less