Symbol Grounding in Connectionist and Adaptive Agent Models
This paper presents the Cognitive Symbol Grounding framework for modeling language in neural networks and adaptive agent simulations. This approach is characterized by the hypothesis that symbols are directly grounded into the agents’ own categorical representations, whilst at the same time having syntactic relationships with other symbols. The mechanism of grounding transfer is also introduced. This is the process by which the grounding of basic words, acquired via direct sensorimotor experience, is transferred to higher-order words via linguistic descriptions. Various simulations are briefly reviewed to demonstrate the use of the Cognitive Symbol Grounding approach.
KeywordsCategorical Perception Basic Word Linguistic Description Connectionist Approach Robotic Agent
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