Computational Intelligence Methods for Financial Applications. Complex financial problems can be better addressed with software that can learn from available data and adapt to environmental changes. It is therefore essential to develop technologies that enable prediction and optimisation in constrained and dynamic environments. There are currently some limitations in existing business decision support systems despite their ubiquity providing an opportunity for Australia to be at the forefront as ....Computational Intelligence Methods for Financial Applications. Complex financial problems can be better addressed with software that can learn from available data and adapt to environmental changes. It is therefore essential to develop technologies that enable prediction and optimisation in constrained and dynamic environments. There are currently some limitations in existing business decision support systems despite their ubiquity providing an opportunity for Australia to be at the forefront as new standards in the field are developed. Furthermore, the fund management industry (particularly superannuation) is significant to the Australian economy and development of this technology has the potential to enhance its performance and reputation.Read moreRead less
Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected ....Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected outcomes include the
development of software that allows the solution of hitherto unsolved machine
learning problems, and the ability to solve problems larger than those solvable
by the current generation of machine learning tools.Read moreRead less
Developing optimal synthesis strategies in distributed expert systems. The aim of this project is to investigate synthesis strategies in distributed expert systems (DESs). Such strategies are used to synthesize multiple solutions to the same task from different experts (either human experts or expert systerms) in order to obtain the final solution to the task. These strategies could be used in a wide application of domains such as insurance agencies and medical diagnosis systems. The expected ....Developing optimal synthesis strategies in distributed expert systems. The aim of this project is to investigate synthesis strategies in distributed expert systems (DESs). Such strategies are used to synthesize multiple solutions to the same task from different experts (either human experts or expert systerms) in order to obtain the final solution to the task. These strategies could be used in a wide application of domains such as insurance agencies and medical diagnosis systems. The expected outcomes are to develop computational strategies, neural network strategies, and case-based strategies for solving different synthesis cases.Read moreRead less
The Next Step in Intelligent Decision-Support Systems (IDSS): Systems that Learn and Adapt. This project will benefit Australia's scientific knowledge and technology base in the areas of evolutionary computation, business intelligence, and decision management. The outcomes will advance Australian companies and organisations, as many common yet complex business problems can be better addressed with systems that automatically learn and adapt to environmental changes. Such complex business problems ....The Next Step in Intelligent Decision-Support Systems (IDSS): Systems that Learn and Adapt. This project will benefit Australia's scientific knowledge and technology base in the areas of evolutionary computation, business intelligence, and decision management. The outcomes will advance Australian companies and organisations, as many common yet complex business problems can be better addressed with systems that automatically learn and adapt to environmental changes. Such complex business problems include dynamic scheduling (in the manufacturing sector), resource allocation optimisation (in the defence, mining, and agriculture sectors), and network design optimisation (in the telecommunications and energy sectors).Read moreRead less
The Physics of Network Computation. This project combines expertise in nonlinear soliton physics and computational sciences in order to provide new insights into the physics of network computation. Our proposal addresses the mathematics and computer modelling underlying nonconscious problem solving. We develop a new template concept, the meta-mode, which embodies the network structure of knowledge and the linking mechanisms, which underpin human creativity. We establish the optimal connectiv ....The Physics of Network Computation. This project combines expertise in nonlinear soliton physics and computational sciences in order to provide new insights into the physics of network computation. Our proposal addresses the mathematics and computer modelling underlying nonconscious problem solving. We develop a new template concept, the meta-mode, which embodies the network structure of knowledge and the linking mechanisms, which underpin human creativity. We establish the optimal connectivity distributions to preserve distinct pattern classes yet allow model radical shifts in paradigms, and develop algorithms for autonomous connectivity optimisation. We investigate nonlinear process such as solitons and random Boolean networks as realisations of these principles.Read moreRead less
Meta-Ontology based Protocols for the Cooperation in Heterogeneous Agent Systems. Cooperation and communication are two important research issues in the area of multi-agent systems and distributed artificial intelligence. The aim of this project is to study and develop meta-ontology based protocols for cooperation in multi-agent systems. The outcomes of the project include semantics of meta-ontology, a conceptual model to encompass the defined semantics, implementation of the model, and test res ....Meta-Ontology based Protocols for the Cooperation in Heterogeneous Agent Systems. Cooperation and communication are two important research issues in the area of multi-agent systems and distributed artificial intelligence. The aim of this project is to study and develop meta-ontology based protocols for cooperation in multi-agent systems. The outcomes of the project include semantics of meta-ontology, a conceptual model to encompass the defined semantics, implementation of the model, and test results in an open environment. The Key Laboratory of Intelligent Information Processing at Institute of Computing Technology, Chinese Academy of Sciences, is very active in the area of distributed artificial intelligence and agent technologies, and is an ideal partner for this project.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
Sentiment detection from opinion surveys -- the quest for customer and employee satisfaction. The research will yield improved international standing through scientific advances disseminated through high impact refereed publications and open source software. The advances made through the application of sophisticated probabilistic techniques to Language Technology problems will attract post-graduate students, and promote commercial interest. The demonstration prototype will provide proof of conce ....Sentiment detection from opinion surveys -- the quest for customer and employee satisfaction. The research will yield improved international standing through scientific advances disseminated through high impact refereed publications and open source software. The advances made through the application of sophisticated probabilistic techniques to Language Technology problems will attract post-graduate students, and promote commercial interest. The demonstration prototype will provide proof of concept of an application that enables business intelligence to automatically process free-form feedback from customers and employees, with resultant recommendations leading to increased customer and employee satisfaction. The applicability of the outcomes of this research to service industries will further improve Australia's service reputation.Read moreRead less
Query interpretation and response generation in large on-line resources. The unprecedented information explosion associated with the evolution of the Internet makes salient the challenge of providing users with answers to queries posed to Internet resources. The proposed project will apply machine learning and reasoning under uncertainty techniques to leverage the large amount of data found in the Internet in order to perform three tasks: (1) infer users' informational goals from their questions ....Query interpretation and response generation in large on-line resources. The unprecedented information explosion associated with the evolution of the Internet makes salient the challenge of providing users with answers to queries posed to Internet resources. The proposed project will apply machine learning and reasoning under uncertainty techniques to leverage the large amount of data found in the Internet in order to perform three tasks: (1) infer users' informational goals from their questions, (2) modify questions to improve the accuracy of retrieval engines, and (3) compose concise replies from the retrieved documents. The envisioned outcome of this project is a system that will generate replies to questions posed to on-line resources.Read moreRead less
Deja-Vu -- A mechanism for constructing dialogue memory for resource-bounded agents. The ability to provide contextual information during interactions with computer systems has great potential to improve the overall experience for users. We propose to develop such an ability in the form of an automatically generated, continuously updated ``dialogue memory'', which may reside at server sites or in the PDAs of individual users. This memory will be generated by means of a novel approach which combi ....Deja-Vu -- A mechanism for constructing dialogue memory for resource-bounded agents. The ability to provide contextual information during interactions with computer systems has great potential to improve the overall experience for users. We propose to develop such an ability in the form of an automatically generated, continuously updated ``dialogue memory'', which may reside at server sites or in the PDAs of individual users. This memory will be generated by means of a novel approach which combines Natural Language techniques to extract dialogue features, model-selection techniques to cluster related dialogues, and cognitive modeling techniques to prune the resultant memories. The implemented computer system will be tested in the domain of trouble-shooting dialogues.Read moreRead less