Transaction Oriented Computational Models for Multi Agent Systems. Agent systems are a very promising technology for constructing complex, large-scale software. Australian researchers have made key
contributions in this area, particularly with reference to one mature and commonly adopted agent architecture known as BDI (Belief, Desire, Intention). To make this technology suitable for use in advanced applications, it has to be provided with robust and predictable behaviour. This project wil ....Transaction Oriented Computational Models for Multi Agent Systems. Agent systems are a very promising technology for constructing complex, large-scale software. Australian researchers have made key
contributions in this area, particularly with reference to one mature and commonly adopted agent architecture known as BDI (Belief, Desire, Intention). To make this technology suitable for use in advanced applications, it has to be provided with robust and predictable behaviour. This project will address that need by designing and implementing a novel agent language for BDI, based on contributions using transactional concepts for agents developed at The University of Melbourne. This will contribute to the development of robust and predictable agent software, that can be used in complex and large scale applications of the future.
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Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior ....Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior knowledge of physical environments, etc.) and combine the advantages of feature based high-level vision methods with low-level machine learning techniques.
This proposal is part of a joint IST project with partners from the European Union.Read moreRead less
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
Efficient Strategies for Mining Negative Association Rules. Negative association rules (NAR) catch mutually-exclusive correlations
among items. They play important roles just as traditional association
rules (TAR) do. For example, in stock market surveillance based on alert logs, NARs detect which alerts are false. There are essential differences between mining TARs and NARs because NARs are hidden in infrequent itemsets. This research will develop efficient strategies for mining NARs in datab ....Efficient Strategies for Mining Negative Association Rules. Negative association rules (NAR) catch mutually-exclusive correlations
among items. They play important roles just as traditional association
rules (TAR) do. For example, in stock market surveillance based on alert logs, NARs detect which alerts are false. There are essential differences between mining TARs and NARs because NARs are hidden in infrequent itemsets. This research will develop efficient strategies for mining NARs in databases. These strategies are expected to be about ten times faster than existing ones. This project will deliver database-independent and high-performance mining algorithms for decision-making. The results can benefit Australian marketing and financial companies as well as health and security departments for smart information use.Read moreRead less
Multiple Data Source Discovery: Group Interaction Approach. This project will develop new technology and theory to identify and evaluate incomplete data. It will deliver a high-performance group-interaction based global pattern discovery system that enables decision-makers (like doctors) to access valuable implicit information that is contained in their data but not currently accessible. Mining group interactions will greatly extend the scope of pattern discovery and new product evaluation. The ....Multiple Data Source Discovery: Group Interaction Approach. This project will develop new technology and theory to identify and evaluate incomplete data. It will deliver a high-performance group-interaction based global pattern discovery system that enables decision-makers (like doctors) to access valuable implicit information that is contained in their data but not currently accessible. Mining group interactions will greatly extend the scope of pattern discovery and new product evaluation. The outcomes of the project will lead to better diagnostic decisions and will lead to increased efficiency in Australian Industries.Read moreRead less
Effective Techniques and Methodologies for Multi-Database Mining. This project develops a high-performance multi-database mining system. This project is significant because (1) it is imperative due to a great deal of multi-databases widely used in organizations; (2) it is difficult due to essential differences between mono- and multi-databases; (3) existing multi-database mining techniques are inadequate; and (4) the new mining strategies in this project can make a vast improvement of the abilit ....Effective Techniques and Methodologies for Multi-Database Mining. This project develops a high-performance multi-database mining system. This project is significant because (1) it is imperative due to a great deal of multi-databases widely used in organizations; (2) it is difficult due to essential differences between mono- and multi-databases; (3) existing multi-database mining techniques are inadequate; and (4) the new mining strategies in this project can make a vast improvement of the ability and performance of multi-database mining systems. The expected outcomes are: an application-independent database classification, a local instance analysis and a prototype system. These proposed techniques are innovative, effective and efficient in identifying novel patterns from multi-databases.Read moreRead less
Ontology-Based Group Pattern Discovery Systems for Mining Multiple Data Sources. This project will aim at the frontier technologies development for practical techniques in the context of real multiple-data-source mining systems, including stock data and e-business data analysis. It will bring Australian individuals and organizations (i) high quality information from multiple data sources and (ii) automatically pattern discovery systems for tackling the multiple data source problem. This will lea ....Ontology-Based Group Pattern Discovery Systems for Mining Multiple Data Sources. This project will aim at the frontier technologies development for practical techniques in the context of real multiple-data-source mining systems, including stock data and e-business data analysis. It will bring Australian individuals and organizations (i) high quality information from multiple data sources and (ii) automatically pattern discovery systems for tackling the multiple data source problem. This will lead to greatly enhance the international competition of Australian companies and significantly reduce investing risks. 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.