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My research focuses on the explainability of Artificial Intelligence and Reinforcement Learning in particular.
I find it fascinating how Reinforcement Learning algorithms can independently develop strategies based only on observations and rewards. In some cases, the agents even develop new strategies that even humans have not yet considered ( e.g. by the chess computer AlphaZero). However, since only the goal of the agents is defined, it is often not clear what exactly the learned strategies look like. This is exacerbated by the use of modern machine learning techniques, which achieve considerable success but are also very opaque.
The goal of my research is to develop new algorithms that make the behaviour of intelligent agents explainable to users and thus facilitate the cooperation between humans and computers.
研究兴趣
论文共 26 篇作者统计合作学者相似作者
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Lecture Notes in Computer Sciencepp.320-332, (2023)
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LAK (2023): 253-262
Educational Data Mining (2023)
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HCI (40)pp.381-397, (2023)
Frontiers in artificial intelligence (2022): 903875
arxiv(2022)
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