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Semantic Interpretation of Multi-Modal Human-Behaviour Data - Making Sense of Events, Activities, Processes.

, and . Künstliche Intell., 31 (4): 317-320 (2017)

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Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data., , , , and . Frontiers Robotics AI, (2021)Alfie: An Interactive Robot with a Moral Compass., , , and . CoRR, (2020)Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning., , , , , and . Mach. Learn., 83 (2): 133-135 (2011)A unifying view of explicit and implicit feature maps of graph kernels., , , , and . Data Min. Knowl. Discov., 33 (6): 1505-1547 (2019)Conditional sum-product networks: Modular probabilistic circuits via gate functions., , , , , , and . Int. J. Approx. Reason., (2022)Learning to transfer optimal navigation policies., , , , and . Adv. Robotics, 21 (13): 1565-1582 (2007)Statistical Relational Artificial Intelligence: Logic, Probability, and Computation, , , and . Synthesis Lectures on Artificial Intelligence and Machine Learning Morgan & Claypool Publishers, (2016)Effectively creating weakly labeled training examples via approximate domain knowledge, , , , , and . Inductive Logic Programming, Springer, (2015)Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models, , , , , , and . Proceedings of ECML, (2010)Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text, , , , , and . Statistical Relational Artificial Intelligence, Papers from the 2013 AAAI Workshop, Bellevue, Washington, USA, July 15, 2013, volume WS-13-16 of AAAI Workshops, AAAI, (2013)