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Field of Research : Autonomous agents and multiagent systems
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Autonomous agents and multiagent systems (5)
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT230100563

    Funder
    Australian Research Council
    Funding Amount
    $1,113,857.00
    Summary
    Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating .... Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating and influencing attention allocation, and enable applications where content consumers, producers, hosting platforms and regulatory bodies are each empowered with their share of influence in the attention market.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT220100650

    Funder
    Australian Research Council
    Funding Amount
    $879,586.00
    Summary
    Improving the performance of Australian social insurance schemes. Applying methods from computational social science, this project aims to develop a novel, multi-level modeling framework to assist transport injury, workplace injury and disability insurance schemes consistently achieve and maintain standards of high performance as recognised by international benchmarks. By creating a virtual laboratory for policy-makers and scheme managers, it expects to generate a comprehensive understanding of .... Improving the performance of Australian social insurance schemes. Applying methods from computational social science, this project aims to develop a novel, multi-level modeling framework to assist transport injury, workplace injury and disability insurance schemes consistently achieve and maintain standards of high performance as recognised by international benchmarks. By creating a virtual laboratory for policy-makers and scheme managers, it expects to generate a comprehensive understanding of mechanisms driving insurance scheme performance, enabling comparison of anticipated outcomes in response to legislative changes, policy changes and management decisions. The project aims to help schemes avoid human and financial failure, benefitting people with injuries and disabilities while reducing scheme costs.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT220100669

    Funder
    Australian Research Council
    Funding Amount
    $805,961.00
    Summary
    Swarm construction: ant-inspired processes for teams of building robots. Construction and manufacturing can be dangerous, wasteful industries—prime candidates for automation by teams of mobile robot builders. However, our understanding of how to program robots for teamwork is limited. This project aims to understand how colonies of weaver ants build complex nest structures, using novel 3D-imaging and ant tracking techniques. The anticipated outcomes of the project are i) a framework for how indi .... Swarm construction: ant-inspired processes for teams of building robots. Construction and manufacturing can be dangerous, wasteful industries—prime candidates for automation by teams of mobile robot builders. However, our understanding of how to program robots for teamwork is limited. This project aims to understand how colonies of weaver ants build complex nest structures, using novel 3D-imaging and ant tracking techniques. The anticipated outcomes of the project are i) a framework for how individual-level behaviour drives structure-level outcomes, applicable to many complex systems, and ii) novel software and hardware for robot swarms that can 3D-print structures using ant inspired teamwork strategies. Benefits of the project include new construction technologies that are safer, greener, cheaper and faster.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240100506

    Funder
    Australian Research Council
    Funding Amount
    $521,114.00
    Summary
    Interactions of Human and Machine Intelligence in Modern Economic Systems. Much of modern economic systems are driven by machine-machine and machine-human interactions that happens rapidly at large scale. But such interactions are often opaque and can have negative or catastrophic consequences, such as market plunges with no apparent economic reasons in financial trading, content recommendations that promote extremism, algorithms in gig economy leading to worker exploitation and wasted resources .... Interactions of Human and Machine Intelligence in Modern Economic Systems. Much of modern economic systems are driven by machine-machine and machine-human interactions that happens rapidly at large scale. But such interactions are often opaque and can have negative or catastrophic consequences, such as market plunges with no apparent economic reasons in financial trading, content recommendations that promote extremism, algorithms in gig economy leading to worker exploitation and wasted resources. This project aims for new theoretical results and algorithms at the intersection computational economics, game theory, and dynamical systems, that establish conditions under which the economic systems are stable, propose mechanisms that make the interactions more fair, transparent and aligned with human values.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240100753

    Funder
    Australian Research Council
    Funding Amount
    $529,615.00
    Summary
    Autonomous Discovery of Green Inhibitors. The project aims to develop autonomous material design by integrating evolutionary algorithms and robotic experimentation. The project expects to pioneer a new method of materials discovery that could cut discovery times to 20% of traditional methods. Its expected to have significance through its discovery of new classes of corrosion inhibitors that are safe to both humans and the environment. The expected outcomes of this project will be a rapid disc .... Autonomous Discovery of Green Inhibitors. The project aims to develop autonomous material design by integrating evolutionary algorithms and robotic experimentation. The project expects to pioneer a new method of materials discovery that could cut discovery times to 20% of traditional methods. Its expected to have significance through its discovery of new classes of corrosion inhibitors that are safe to both humans and the environment. The expected outcomes of this project will be a rapid discovery methodology that can be used across materials science and new classes of safe corrosion inhibitors. This should provide significant benefits to workplace n safety and the environmental impact of the coatings industry while also increasing the rapid of innovation of new materials.
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