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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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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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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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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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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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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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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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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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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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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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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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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