A Grounded Approach to Modeling Generic Knowledge Acquisition
This work addresses the challenge of simulating cognitively plausible concept development in AI language acquisition, though it appears incremental as it builds on an existing framework.
The paper tackled the problem of modeling how generic language statements, which express generalizations about categories, are acquired during language learning, by extending a computational framework with a concept network to encode such knowledge and demonstrate its acquisition through tasks.
We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.