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AMBER is a model of first language acquisition that improves its performance through a process of error recovery. The model is implemented in ACTG, an adaptive production system language. AMBER starts with the ability to say only one word at a time, but adds rules for inserting additional words in the correct order, based on comparisons between predicted and observed sentences. These insertion rules may be overly general and lead to errors of commission; in turn, these lead to more conservative rules with additional conditions. AMBER's learning mechanisms account for many of the developments observed in children's speech.
Pat Langley (Tue,) studied this question.
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