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  • Generative modelling with neural probabilistic circuits

    Project ID: STAI-CDT-2023-KCL-20
    Themes: AI Planning, Logic, Verification
    Supervisor: David Watson

    The current state of the art in generative modelling is dominated by neural networks. Despite their impressive performance on many benchmark tasks, these algorithms do not provide tractable inference for common and...

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  • Computational Social Choice and Machine Learning for Ethical Decision Making

    Project ID: STAI-CDT-2023-KCL-5
    Themes: AI Planning, Argumentation, Norms, Reasoning
    Supervisor: Maria Polukarov

    The problem of ethical decision making presents a grand challenge for modern AI research. Arguably, the main obstacle to automating ethical decisions is the lack of a formal specification of ground-truth ethical principles,...

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  • Detecting Deception and Manipulation in Planning and Explanation Systems

    Project ID: STAI-CDT-2023-KCL-2
    Themes: AI Planning
    Supervisor: Martim Brandao

    Planning algorithms are used in a variety of contexts, from navigation apps to recommendation algorithms, robot vacuums, autonomous vehicles, etc.Companies using such algorithms have financial incentives to manipulate (or...

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  • A Critical and Inclusive Approach to Robotics

    Project ID: STAI-CDT-2023-KCL-3
    Themes: AI Planning
    Supervisor: Martim Brandao

    Robotics are already being used in warehouses, factories, super markets, homes, hazardous sites and other applications. While many issues of stereotypes, disparate impact, and harmful impact of AI have been brought to the...

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  • Formal Reasoning about Golog Programs

    Project ID: STAI-CDT-2022-KCL-10
    Themes: AI Planning, Logic, Verification
    Supervisor: Mohammad Abdulaziz

    Constructing a world-model is a fundamental part of model-based AI, e.g. planning. Usually, such a model is constructed by a human modeller and it should capture the modeller’s intuitive understanding of the world dynamics...

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  • Neuro-Symbolic Policy Learning and Representation for Interpretable and Formally-Verifiable Reinforcement Learning

    Project ID: STAI-CDT-2021-IC-24
    Themes: AI Planning, Logic, Verification
    Supervisor: Francesco Belardinelli

    The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including...

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  • Run-time Verification for Safe and Verifiable AI

    Project ID: STAI-CDT-2021-IC-23
    Themes: AI Planning, Logic, Verification
    Supervisor: Francesco Belardinelli

    The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including...

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  • Reward Synthesis from Logical Specifications

    Project ID: STAI-CDT-2021-IC-22
    Themes: AI Planning, Logic, Verification
    Supervisor: Francesco Belardinelli

    The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including...

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  • Synthesizing and revising plans for autonomous robot adaptation

    Project ID: STAI-CDT-2021-IC-20
    Themes: AI Planning, Logic, Verification
    Supervisor: Dalal Alrajeh

    AI Planning is concerned with producing plans that are guaranteed to achieve a robot’s goals, assuming the pre-specified assumptions about the environment in which it operates hold. However, no matter how detailed these...

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  • Symbolic machine learning techniques for explainable AI

    Project ID: STAI-CDT-2021-KCL-15
    Themes: AI Planning, Verification
    Supervisor: Kevin Lano

    Machine learning (ML) approaches such as encoder-decoder networks and LSTM have been successfully used for numerous tasks involving translation or prediction of information (Otter et al, 2020). However, the knowledge...

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  • Goal-based explanations for autonomous systems and robots

    Project ID: STAI-CDT-2021-KCL-11
    Themes: AI Planning
    Supervisor: Gerard Canal, Andrew Coles

    Autonomous systems such as robots may become another appliance found in our homes and workplaces. In order to have such systems helping humans to perform their tasks, they must be as autonomous as possible, to prevent...

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  • Neural-symbolic Reinforcement Learning.

    Project ID: STAI-CDT-2021-IC-2
    Themes: AI Planning, Logic
    Supervisor: Alessandra Russo

    Recent advances in deep reinforcement learning (DRL) have allowed computer programs to beat humans at complex games like Chess or Go years before the original projections. However, the SOTA in DRL misses out on some of the...

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