Designing an Intelligent Process Operation Management System. The complexity and sophistication of current generation of industrial processes, and the growing need for autonomous agents that control physical systems, motivate the need for the development of an intelligent process operation management system. In this project, the innovative integration of theories from different scientific fields (computer systems, process engineering, systems and control engineering) provides an excellent platfo ....Designing an Intelligent Process Operation Management System. The complexity and sophistication of current generation of industrial processes, and the growing need for autonomous agents that control physical systems, motivate the need for the development of an intelligent process operation management system. In this project, the innovative integration of theories from different scientific fields (computer systems, process engineering, systems and control engineering) provides an excellent platform for development of a smart data management tool, to oversee the major operational tasks within the plant and help the operators and engineers to make more informed decisions. Direct application of the techniques developed in this study to a pilot case study, could be used as a benchmark to show the potential benefits that can be gained through smart information use and data management.Read moreRead less
Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ....Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ineffective strategies for addressing land degradation; inappropriately targeted public health expenditure; expensive development and clinical trialing of drugs which prove ineffective; and wasted police and security investigations into unfounded suspicions of criminal or terrorist activity.Read moreRead less
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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Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well ....Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well, better safeguarding Australia from disease and crime. This project will also support a young research group of international standing. It will train the involved researchers to attain a high level of proficiency and excellence in machine learning research and development.Read moreRead less
Fault detection and identification in nonlinear complex systems. Complex systems usually comprise a large number of inter-dependent subsystems linked together to perform a certain task. Examples of such systems are power systems, irrigation systems, air traffic control systems, to name a few. Such systems are subject to component failure or malfunction. Total failure can cause an unacceptable financial losses and/or danger to personnel. It is therefore extremely essential, from economic and safe ....Fault detection and identification in nonlinear complex systems. Complex systems usually comprise a large number of inter-dependent subsystems linked together to perform a certain task. Examples of such systems are power systems, irrigation systems, air traffic control systems, to name a few. Such systems are subject to component failure or malfunction. Total failure can cause an unacceptable financial losses and/or danger to personnel. It is therefore extremely essential, from economic and safety view points, that a way be found to ensure reliable and viable operation of complex plants. A first step in achieving this goal is to detect faults on-line and in real-time when they occur and identify their location and characteristics, which is the aim of this project.Read moreRead less
Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internation ....Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internationally. Thus, significant benefits in terms of lower mortality rates and lower long-term disability rates among babies requiring special care is possible. This research will provide the basis for future projects that will support regional hospitals.Read moreRead less
Formalising and automating the elicitation and reconciliation of requirements from multiple stakeholders. It is well recognised that requirements specifications are often error-prone and that it is much cheaper to detect and fix these errors early in the software development life cycle than later. A major problem with requirements determination is that each and every stakeholder has his/her own representation of the enterprise reality. This project seeks to take these views and use set-theore ....Formalising and automating the elicitation and reconciliation of requirements from multiple stakeholders. It is well recognised that requirements specifications are often error-prone and that it is much cheaper to detect and fix these errors early in the software development life cycle than later. A major problem with requirements determination is that each and every stakeholder has his/her own representation of the enterprise reality. This project seeks to take these views and use set-theoretical techniques from Formal Concept Analysis (FCA) to automatically generate and compare the underlying conceptual models. A process model based on FCA has been proposed which we will extend and empirically evaluate in this project. The result will be a more rigorous and yet pragmatic approach to requirements engineering which offers the greatest economic leverage.Read moreRead less
Personalised Ontology Learning and Mining for Web Information Gathering. The project will provide a flexible framework for a sound theoretical model of personalised systems. It will significantly influence the development of personalised Web services and many leading industry organisations that attempt to deliver personalised services to their valuable customers. The proposed project will also strengthen the pre-existing international collaboration networks. It will establish Australian researc ....Personalised Ontology Learning and Mining for Web Information Gathering. The project will provide a flexible framework for a sound theoretical model of personalised systems. It will significantly influence the development of personalised Web services and many leading industry organisations that attempt to deliver personalised services to their valuable customers. The proposed project will also strengthen the pre-existing international collaboration networks. It will establish Australian researchers leading position in the related research fields and communities, and provide an established paradigm for other researchers to follow. In addition, the project will provide significant contributions to Australian National Research Priority in the areas of Smart Information Use.Read moreRead less
Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach ....Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach for buildingmachine translation systems by extending the unorthodox approach of Ripple-Down Rules, which proved very successful for building expert systems in the medical domain.It is intended to build a machine translation system by integrating the knowledge from many experts.Read moreRead less
Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-speci ....Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.Read moreRead less