• 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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  • Computational social choice and machine learning for ethical decision making

    Project ID: STAI-CDT-2021-KCL-11a
    Themes: AI Planning, Argumentation, Logic, Norms
    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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  • 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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  • Model Checking Agents that Learn

    Project ID: STAI-CDT-2021-IC-3
    Themes: AI Planning, Verification
    Supervisor: Francesco Belardinelli

    In Reinforcement Learning (RL) autonomous agents have typically to choose their actions in order to maximise some notion of cumulative reward [1]. Tools and techniques for RL have been applied successfully to domain as...

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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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  • Enhancing Scale and Performance of Safe and Trusted Multi-Agent Planning

    Project ID: STAI-CDT-2021-IC-5
    Themes: AI Planning
    Supervisor: Wayne Luk

    Cooperative Multi-Agent Planning (MAP) is a topic in symbolic artificial intelligence (AI). In a cooperative MAP system, multiple agents collaborate to achieve a common goal. A cooperative MAP solver produces...

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  • Safe Rational Interactions in Data-driven Control

    Project ID: STAI-CDT-2021-IC-8
    Themes: AI Planning, Logic, Verification
    Supervisor: Alessio Lomuscio

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

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  • Correct-by-construction domain-specific AI planners

    Project ID: STAI-CDT-2021-KCL-4
    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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