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  • Safe game play in data-driven control

    Project ID: STAI-CDT-2020-IC-39
    Themes: AI Planning, Verification
    Supervisor: Alessio Lomuscio

    In autonomous and multi-agent systems players are normally assumed rational and cooperating and/or competing in groups to achieve their overall objectives. Useful methods to study the resulting interactions come from game...

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  • Verification of AI-based perception systems

    Project ID: STAI-CDT-2020-IC-34
    Themes: AI Planning, Verification
    Supervisor: Alessio Lomuscio

    State-of-the-art present perception systems, including those based on Lidar or cameras, are increasingly being used in a range of critical applications including security and autonomous vehicles. While the present deep...

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  • Trusted test suites for safe agent-based simulations

    Project ID: STAI-CDT-2020-KCL-11
    Themes: AI Planning, Verification
    Supervisor: Steffen Zschaler

    Agent-based models (ABMs) are an AI technique to help improve our understanding of complex real-world interactions and their ”emergent behaviour”. ABMs are used to develop and test theories or to explore how...

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  • Correct-by-construction domain-specific AI planners

    Project ID: STAI-CDT-2020-KCL-10
    Themes: AI Planning, Verification
    Supervisor: Steffen Zschaler

    When using complex algorithms to make decisions within autonomous systems, the weak link is the abstract model used by the algorithms: any errors in the model may lead to unanticipated behaviour potentially risking...

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  • Digital twins for the verification of learning and adaptive software

    Project ID: STAI-CDT-2020-IC-41
    Themes: AI Planning, Verification
    Supervisor: Antonio Filieri

    Learning and decision-making AI components are gaining popularity as enablers of modern adaptive software. Common uses include, for example, the classification or regression of incoming data (e.g., face recognition), the...

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  • Safety evaluation of intelligent transportation systems using agent-based models

    Project ID: STAI-CDT-2020-IC-42
    Themes: AI Planning, Verification
    Supervisor: Wayne Luk

    Recent intelligent transportation systems reduce traffic congestion and improve the overall efficiency of traffic networks. Such systems often involve complex algorithmic logic, large-scale traffic networks, and a high...

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  • Neural-symbolic learning: a solution for generalisable and explainable AI

    Project ID: STAI-CDT-2020-IC-43
    Themes: AI Planning, Logic
    Supervisor: Alessandra Russo

    This research proposal focuses on developing a novel hybrid neural-symbolic learning approach that combines the capabilities of deep learning methods in extracting features from unstructured data with the ability of...

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  • Abstract Interpretation for Safe Machine Learning

    Project ID: STAI-CDT-2020-IC-7
    Themes: AI Planning, Verification
    Supervisor: Sergio Maffeis

    Machine learning (ML) techniques such as Support Vector Machines, Random Forests and Neural Networks are being applied with great success to a wide range of complex and sometimes safety-critical tasks. Recent research in...

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  • A model-based verification for safe and trusted concurrent robotics systems

    Project ID: STAI-CDT-2020-IC-21
    Themes: AI Planning, Logic, Verification
    Supervisor: Nobuko Yoshida

    SUMMARY Robotics applications involve programming concurrent components synchronising through messages while simultaneously executing motion actions that control the state of the physical world. Today, these applications...

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  • A Novel Model-driven AI Paradigm for Intrusion Detection

    Project ID: STAI-CDT-2020-KCL-26
    Themes: AI Planning, Logic, Verification
    Supervisor: Fabio Pierazzi

    Intrusion Detection Systems (IDSs) are commonly deployed in networks and hosts to identify malicious activities representing misuse of computer systems. The numbers and types of attacks have been constantly increasing, and...

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  • Probabilistic Abstract Interpretation of Deep Neural Networks

    Project ID: STAI-CDT-2020-IC-30
    Themes: AI Planning, Verification
    Supervisor: Herbert Wiklicky

    The extraction of (symbolic) rules which describe the operation of (deep) neural networks which have been trained to perform a certain task is central to explaining their inner workings in order to judge their correctness,...

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  • Verifying Emergence Properties of Robotic Swarms

    Project ID: STAI-CDT-2020-IC-31
    Themes: AI Planning, Logic, Verification
    Supervisor: Alessio Lomuscio

    The effective development and deployment of single-robot systems is increasingly shown to be problematic in a variety of application domains including search and rescue, remote exploration, de-mining, etc. These and other...

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