A significant portion of early language learning can be viewed as an associative learning problem. We investigate the use of associative language learning based on the principle that words convey Shannon information about the environment. We discuss the shortcomings in representation used by previous associative word learners and propose a functional representation that not only denotes environmental categories, but serves as the basis for activities and interaction with the environment. We present experimental results with an autonomous agent acquiring language
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