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Learning Words by Imitating
Abstract
This chapter proposes a single imitation-learning algorithm capable of simultaneously learning linguistic as well as nonlinguistic tasks, without demonstrations being labeled. A human demonstrator responds to an environment that includes the behavior of another human, called the interactant, and the algorithm must learn to imitate this response without being told what the demonstrator was responding to (for example, the position of an object or a speech utterance of the interactant). Since there is no separate symbolic language system, the symbol grounding problem can be avoided/dissolved. The types of linguistic behavior explored are action responses, which includes verb learning but where actions are generalized to include such things as communicative behaviors or internal cognitive operations. Action responses to object positions are learnt in the same way as action responses to speech utterances of an interactant. Three experiments are used to validate the proposed algorithm.
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