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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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  • Explainable Safety, Security and Trust in Human-AI Systems

    Project ID: STAI-CDT-2021-KCL-13
    Themes: AI Planning, AI Provenance, Logic, Norms, Verification
    Supervisor: Luca Vigano

    Explanations can help all the stakeholders of AI systems and Cybersystems to make better choices. In particular, they can help human users to understand and trust the choices of autonomous systems or to interact in a safe...

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  • Goal-based explanations for autonomous systems and robots

    Project ID: STAI-CDT-2021-KCL-11
    Themes: AI Planning
    Supervisor: 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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  • User-aware plan explanation generation for human-robot interaction

    Project ID: STAI-CDT-2021-KCL-10
    Themes: AI Planning
    Supervisor: Oya Celiktutan

    Robots are progressing out from research laboratories into human environments, motivated by addressing the societal challenges such as aging, loneliness, education and many more. All of such applications requires that...

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  • Service Robot Adaptation to Users with Different Abilities

    Project ID: STAI-CDT-2021-KCL-9
    Themes: AI Planning
    Supervisor: Matteo Leonetti

    Service robots are expected to interact with users in a number of scenarios, from homes to offices and hospitals. Research in service robotics is constantly progressing towards generally more competent autonomous robots,...

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  • Discrete-continuous hybrid planning for adaptive systems

    Project ID: STAI-CDT-2021-IC-15
    Themes: AI Planning, Reasoning, Verification
    Supervisor: Antonio Filieri

    Adaptive cyber-physical systems rely on the composition and coordinated interaction of different decision-making procedures, each typically realized with specific AI methods. Cyber components capabilities and semantics are...

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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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  • Data-Driven and Explainable Discrete Optimization for Effective Transportation in Healthcare

    Project ID: STAI-CDT-2021-KCL-3
    Themes: AI Planning, Argumentation, Scheduling
    Supervisor: Dimitrios Letsios

    This project aims to contribute to the development of safe and trusted, artificially intelligent transportation in healthcare. The London Ambulance Service (LAS) operates more than 1100 ambulances to respond to medical...

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