Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
Effective Fuzzy Systems for Complex Structured Data Using Fuzzy Signatures. We are developing systematic, heuristic and mathematical techniques to produce effective fuzzy systems for complex structured data. Many or most real world problems have data which has interdependent sub-components depending on the context (eg only female patients need be tested for pregnancy), and often has missing components. Our techniques use fuzzy signatures to extend simple fuzzy systems to deal with data with such ....Effective Fuzzy Systems for Complex Structured Data Using Fuzzy Signatures. We are developing systematic, heuristic and mathematical techniques to produce effective fuzzy systems for complex structured data. Many or most real world problems have data which has interdependent sub-components depending on the context (eg only female patients need be tested for pregnancy), and often has missing components. Our techniques use fuzzy signatures to extend simple fuzzy systems to deal with data with such complex (sub-)structure. This produces effective fuzzy systems with wide applicability to real problems, in telecommunications, and petroleum reservoir data.Read moreRead less
Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour. Predicting and modelling customer behaviour enables considerable savings in the telecommunications industry and elsewhere. The resulting predictive models facilitate identifying novice users, identifying fraud, responding to users' needs, guiding and advising users, and forwarding useful information.
We consider two cutting-edge data mining approaches, Minimum Message Length (developed and led by ....Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour. Predicting and modelling customer behaviour enables considerable savings in the telecommunications industry and elsewhere. The resulting predictive models facilitate identifying novice users, identifying fraud, responding to users' needs, guiding and advising users, and forwarding useful information.
We consider two cutting-edge data mining approaches, Minimum Message Length (developed and led by Monash) and Support Vector Machines, in order to create efficient tailor-made software.
Our software will respond to specific groups of users, and their changes over time, rather than just the average user. Moreover, it will integrate the functionalities of existing individual data mining software.Read moreRead less
Resource-bounded adaptive inference of accurate conditional probability estimates from data. This project will develop machine learning techniques with a valuable new capability: the ability to produce estimates of complex conditional probabilities to varying levels of expected accuracy depending upon the constraints of available computational resources. This will provide significant competitive advantage to developers of many types of online application by allowing them to maximise utilisation ....Resource-bounded adaptive inference of accurate conditional probability estimates from data. This project will develop machine learning techniques with a valuable new capability: the ability to produce estimates of complex conditional probabilities to varying levels of expected accuracy depending upon the constraints of available computational resources. This will provide significant competitive advantage to developers of many types of online application by allowing them to maximise utilisation of available computational resources when making inferences from data, together with the flexibility to trade-off accuracy and computing resources during system design. Australia will also benefit by strengthening its machine learning expertise, which is central to many complex and intelligent systems and the booming data mining industry.Read moreRead less
Supporting adaptive, interactive documents. The project will improve comprehensibility of technical material, reduce paper usage, encourage collaborative science, improve the reliability of published science (by allowing post-publication annotation and correction), and improve the accessibility of technical material for readers who are blind or have poor vision. The project also holds considerable potential for supporting Australian companies in the publishing and document processing industries.
Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent sys ....Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent systems that facilitate adaptation and change.
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Development of globally optimal solutions to simultaneous localisation and mapping for robot navigation. Building robots that can operate on their own is one of the potentially transformational technologies of this century. This project will develop algorithms that are well understood and robust to allow the deployment of robots in environments populated with people and in search and rescue operations where global positioning system is not available.
Optimizing steel industry supply chains through constraint and market-oriented programming. Supply chain optimization is a difficult problem, but of considerable significance to business enterprises. Constraint programming technology is a promising approach to solving such problems, but is inadequate in the context of dynamic supply chains. Market-oriented programming solves resource allocation problems by setting up artificial computational economies of trading software agents and holds promise ....Optimizing steel industry supply chains through constraint and market-oriented programming. Supply chain optimization is a difficult problem, but of considerable significance to business enterprises. Constraint programming technology is a promising approach to solving such problems, but is inadequate in the context of dynamic supply chains. Market-oriented programming solves resource allocation problems by setting up artificial computational economies of trading software agents and holds promise both as an optimization tool and as a tool that supports explicit market negotiation. This project seeks to address several open questions relating to the integrated deployment of these two classes of techniques, in the context of building a practical supply chain optimization system for BHP Steel.Read moreRead less
Intelligent Structured Knowledge Source Integration via Software Agents. This project aims to use flexible information agents to integrate the World Wide Web with a machine readable ontology, namely a large, consistent collection of common sense knowledge. The best developed ontology in the world is Cyc. Cyc's repository of general purpose knowledge is rich and stable, but has a major limitation in requiring its knowledge to be hand-entered by experts. The outcomes of the project will be increas ....Intelligent Structured Knowledge Source Integration via Software Agents. This project aims to use flexible information agents to integrate the World Wide Web with a machine readable ontology, namely a large, consistent collection of common sense knowledge. The best developed ontology in the world is Cyc. Cyc's repository of general purpose knowledge is rich and stable, but has a major limitation in requiring its knowledge to be hand-entered by experts. The outcomes of the project will be increased functionality for ontologies, to enable expert reasoning programs wishing to use a formal ontology such as Cyc to have access to the wealth of knowledge on the World Wide Web.Read moreRead less
Managing quality of experience delivery in new generation telecommunications networks with e-negotiation. New generation telecommunications networks are required to support increasingly demanding services, including interactive multimedia and conferencing. The success of these networks will rely on the users' perception of their quality of experience. The management of these networks will rely on the ability of informed decision making that competes effectively for limited resources in such a ....Managing quality of experience delivery in new generation telecommunications networks with e-negotiation. New generation telecommunications networks are required to support increasingly demanding services, including interactive multimedia and conferencing. The success of these networks will rely on the users' perception of their quality of experience. The management of these networks will rely on the ability of informed decision making that competes effectively for limited resources in such a highly dynamic environment. This project will design information distribution strategies and smart decision making agents that negotiate a user's quality of experience using market mechanisms. Designs from this project will be trialled and validated in the partner's commercial networks.Read moreRead less