Robust Intelligence: Rational Decision-Making under Risk and Uncertainty. This project seeks to bridge the gap between theory and practice with an innovative framework for rational decision-making under risk and uncertainty. Intelligent agents exercise profound and growing impact in business and society. However, significant problems arise in intelligent agent deployment as their theoretical underpinnings do not ensure rational decision-making in complex real-world settings. The project aims to ....Robust Intelligence: Rational Decision-Making under Risk and Uncertainty. This project seeks to bridge the gap between theory and practice with an innovative framework for rational decision-making under risk and uncertainty. Intelligent agents exercise profound and growing impact in business and society. However, significant problems arise in intelligent agent deployment as their theoretical underpinnings do not ensure rational decision-making in complex real-world settings. The project aims to open the door to transformational technologies that may drive new entrepreneurial opportunities in agent-based global services.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps ....Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps found on the web. This project will demonstrate the robot's navigation ability by comparing its performance with a human as it learns to find its way around campus by asking for directions, reading signs and maps, and searching the internet for clues.Read moreRead less
Brain-based sensor fusion for navigating robots. This project uses new findings in neuroscience to create robots that can self-determine which of their sensors will best help them learn to navigate in an environment. This technology enables robot systems to be flexibly deployed without prior calibration for wide ranging applications in environments from office buildings to outdoor ecosystems.
Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to deve ....Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.Read moreRead less
Unifying Foundations for Intelligent Agents. This project aims to drive forward the development of rigorous foundations for intelligent agents. The agent framework, the expected utility principle, sequential decision theory, and the information-theoretic foundations of inductive reasoning and machine learning have already brought significant order into the previously heterogeneous scattered field of artificial intelligence. This project aims to investigate an information-theoretic approach towar ....Unifying Foundations for Intelligent Agents. This project aims to drive forward the development of rigorous foundations for intelligent agents. The agent framework, the expected utility principle, sequential decision theory, and the information-theoretic foundations of inductive reasoning and machine learning have already brought significant order into the previously heterogeneous scattered field of artificial intelligence. This project aims to investigate an information-theoretic approach towards a unifying foundation for intelligent agents, which has recently spawned impressive applications. The theory is expected to provide a gold standard and valuable guidance for researchers working on smart software.Read moreRead less
Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
Responsive automated negotiation in open distributed environments. The outcomes of this project will be of central importance to a wide range of application areas such as service economy, smart energy grids and smart transportation. The work proposed here will enable the information technology industry to utilise distributed systems and agent technologies in developing the software-driven knowledge economy of the twenty-first-century.
Automated benthic understanding with multimodal observations. This project aims to deliver cost-effective techniques to explore and monitor marine environments. The project will develop novel methods for classification of large extent, multimodality seafloor surveys consisting of high-resolution visual 3D gigamosaics made of tens of thousands of images coregistered with broad-scale, lower resolution remote sensing data. This knowledge is essential for designing cost-effective, scalable systems t ....Automated benthic understanding with multimodal observations. This project aims to deliver cost-effective techniques to explore and monitor marine environments. The project will develop novel methods for classification of large extent, multimodality seafloor surveys consisting of high-resolution visual 3D gigamosaics made of tens of thousands of images coregistered with broad-scale, lower resolution remote sensing data. This knowledge is essential for designing cost-effective, scalable systems to explore, map and monitor Australia's marine environments. At a broader level, the approach and the techniques developed in this project have the potential to have applications in other areas such as terrestrial and intertidal ecology, extending positive impacts beyond benthic environments.Read moreRead less
Feature reinforcement learning. Agent applications include speech recognition systems, vision systems, search engines, auto-pilots, spam filters, and robots. The research outputs from this project will enable agents to adapt to their environment and automatically, during deployment, acquire much of the knowledge that is currently required to be built in by agent designers.