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• W1990300548 abstract "Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars." @default.
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• W1990300548 date "1996-01-01" @default.
• W1990300548 modified "2023-09-25" @default.
• W1990300548 title "Learning concepts from sensor data of a mobile robot" @default.
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• W1990300548 doi "https://doi.org/10.1007/bf00117448" @default.
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