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Biography
Alessandro Ronca is a researcher in Artificial Intelligence.
His interests cover many areas of Artificial Intelligence and Computer Science. They include Reinforcement Learning, Learning Theory, Automata Learning, Knowledge Representation and Reasoning, Logic (Temporal Logics in particular), Automata Theory, Algebraic Automata Theory, Complexity Theory, Datalog, Database Query Languages.
His current research focuses on three aspects of Artificial Intelligence.
1) Reinforcement learning in domains where an agent must learn to capture temporal patterns over the history of past events. This work has the potential to greatly extend the number of applications where reinforcement learning can be employed.
2) Machine learning models to capture temporal patterns, including Recurrent Neural Networks and Transformers. He studies them both from formal and experimental point of view, assessing their ability to capture temporal patterns.
3) Logics to capture temporal patterns. He is particularly interested in the Transformation Logics, a new family of temporal logics that he has established and that allows for creating hierarchies of increasing expressivity and complexity, with a great potential to match the expressivity-complexity trade-off required by specific applications.
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Works (14)
https://proceedings.neurips.cc/paper_files/paper/2023/file/7bf3e93543a612b75b6373178ba1faa4-Paper-Conference.pdf