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Paper IPM / Cognitive / 8800 |
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Abstract: | |||||||
Learning by imitation can provide agents with a natural and effective means
for transferring knowledge when brain-to-brain connection is infeasible.
Natural mechanisms for imitation demonstrate strong abstraction and
conceptualization capabilities, however, computational models that have been
proposed for imitative learning barely address these fundamental features
Inspired by functions of human brain constituents and exploiting ideas
enthused by mirror neurons and the multi-store model of memory, we propose
a new model for learning by imitation capable of developing relational
concepts. In our model, memory gradually organizes sensory data into
concepts through reinforcement learning and consolidation, while mirror
neurons maintain an extendible repertoire of familiar actions connected to
corresponding concepts. We also discuss the relation between modeling
behavior of concept-oriented agents in terms of mathematical functions and
relevant biological evidence of mirror neurons. Eventually, we evaluate our
method in a phoneme acquisition experiment through real interaction with
humans.
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