Interactive relational reinforcement learning of concept semantics
نویسندگان
چکیده
منابع مشابه
Interactive Relational Reinforcement Learning of Concept Semantics (Extended Abstract)
We propose a novel approach to the machine learning of formal word sense, learned in interaction with human users using a new form of Relational Reinforcement Learning. The envisaged main application area of our framework is humanmachine communication, where a software agent or robot needs to understand concepts used by human users (e.g., in Natural Language Processing, HCI or Information Retri...
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We present a framework for the interactive machine learning of denotational concept semantics in communication between humans and artificial agents. The capability of software agents and robots to learn how to communicate verbally with human users is obviously highly useful in several real-world applications. Whereas the large majority of existing approaches to the machine learning of word sens...
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A key goal in both education and higher-order cognition research is to understand how relational concepts are best learned. In the current work, we present a novel approach for learning complex relational categories – a low-support, interactive discovery interface. The platform, which allows learners to make modifications to exemplars and see the corresponding effects on membership, holds the p...
متن کاملRelational Reinforcement Learning
Reinforcement learning [10] is a subtopic of machine learning that is concerned with software systems that learn to behave through interaction with their environment and receive only feedback on the quality of their current behavior instead of a set of correctly labelled learning examples. Although reinforcement learning algorithms have been studied extensively in a propositional setting, their...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2013
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-013-5344-9