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Noname manuscript No. (will be inserted by the editor) Interactive Relational Reinforcement Learning of Concept Semantics

By Matthias Nickles and Achim Rettinger


the date of receipt and acceptance should be inserted later Abstract We present a framework for the machine learning of denotational concept semantics using a simple form of symbolic interaction of machines with human users. The capability of software agents and robots to learn how to communicate verbally with human users would obviously be highly useful in several real-world applications, and our framework is meant to provide a further step towards this goal. Whereas the large majority of existing approaches to the machine learning of word sense and other language aspects focuses on learning using text corpora, our framework allows for the interactive learning of concepts in a dialog of human and agent, using an approach in the area of Relational Reinforcement Learning. Such an approach has a wide range of possible applications, e.g., the interactive acquisition of semantic categories for the Semantic Web, Human-Computer Interaction, (interactive

Topics: Reinforcement Learning, Concept Learning, Symbol Grounding, Statistical Relational Learning, Interactive Learning, Meaning Disambiguation
Year: 2013
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