Our work focuses on developing, evaluating, and validating novel DRL and IRL methods that allow intelligent agents to perform tasks in collaboration with humans, aligning with human preferences and objectives, also with respect to constraints, promoting AI trustworthiness, safety and humans’ situation-awareness in jointly performing …
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Modelling (complex) systems for decision making using multi-agent reinforcement learning methods (MARL) for the computation of agents’ joint policies for action in critical domains is a priority and challenge here. Specifically, we aim to resolve cases in large-scale and complex settings where agents have conflicting …
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The computation of agreements – or conventions- in agents’ societies via social learning methods, when agents aim to perform tasks in coordination with others is a challenge, especially when agents have to achieve multiple and conflicting goals simultaneously w.r.t. operational constraints, jointly with their peers, …
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Rich and diverse information sources of static or stream structured data do exist. However these are heterogeneous in various aspects: On the ways information is formed, on the ways information is described and shaped, on the ways different aspects of (abstract or concrete) entities are …
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Combining ontologies by discovering semantic associations at any level of abstraction, or decomposing large ontologies in modules w.r.t. logical properties either for re-usability of specifications, efficiency in ontology management, or efficiecy in reasoning, are goals that we need to pursue, especially when we deal with …
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