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Research Topic : Logic
Australian State/Territory : ACT
Australian State/Territory : NSW
Status : Closed
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  • Funded Activity

    Discovery Projects - Grant ID: DP0345643

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    Design of dynamic visual signals. Models of the design of visual signals depend heavily upon analyses of static ornaments. Nothing is known about dynamic visual signals. We will use an array of new techniques to tackle this problem for the first time. Motion analyses will define the task faced by the visual system. Sensory limitations will be measured to identify constraints on signal evolution. Digital video playback studies will assess recognition and explain aspects of signal design. Results .... Design of dynamic visual signals. Models of the design of visual signals depend heavily upon analyses of static ornaments. Nothing is known about dynamic visual signals. We will use an array of new techniques to tackle this problem for the first time. Motion analyses will define the task faced by the visual system. Sensory limitations will be measured to identify constraints on signal evolution. Digital video playback studies will assess recognition and explain aspects of signal design. Results will test the generality of principles that have been developed in studies of female mate choice and extend these ideas to address intra-sexual selection operating through opponent assessment.
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    Funded Activity

    Discovery Projects - Grant ID: DP0343346

    Funder
    Australian Research Council
    Funding Amount
    $159,264.00
    Summary
    Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori .... Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.
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    Funded Activity

    Discovery Projects - Grant ID: DP0208969

    Funder
    Australian Research Council
    Funding Amount
    $258,752.00
    Summary
    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.
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    Funded Activity

    Discovery Projects - Grant ID: DP140104245

    Funder
    Australian Research Council
    Funding Amount
    $358,000.00
    Summary
    Practical Automated Deduction. This project will develop, implement and validate improved methods for automated deduction in decidable fragments of first order logic, also incorporating reasoning in special theories such as arithmetic. It will significantly extend previous work on the model evolution calculus and dynamic semantic resolution, and introduce new techniques that combine these reasoning methods. This work has direct application to reasoning about business rules and about industrial o .... Practical Automated Deduction. This project will develop, implement and validate improved methods for automated deduction in decidable fragments of first order logic, also incorporating reasoning in special theories such as arithmetic. It will significantly extend previous work on the model evolution calculus and dynamic semantic resolution, and introduce new techniques that combine these reasoning methods. This work has direct application to reasoning about business rules and about industrial optimisation problems, and it will motivate and test our systems by means of case studies from both of these areas.
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    Funded Activity

    Discovery Projects - Grant ID: DP120103758

    Funder
    Australian Research Council
    Funding Amount
    $320,000.00
    Summary
    Artificial intelligence meets wireless sensor networks: filling the gaps between sensors using spatial reasoning. Monitoring potential disaster regions and integrating available information with expert knowledge can prevent disasters and save many lives. The outcome of our project is one of the key components for intelligent systems that can autonomously monitor the environment, make the correct inferences and issue appropriate warnings and recommendations.
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    Showing 1-5 of 5 Funded Activites

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