Semantic Representation of Abstract Words in Cognitive Robot Model by using Transitive Inference

Abstract

How do abstract words get meaning? And how could sensorimotor experience based representation be used for abstract words? This is a very important problem for cognitive science, neuroscience and cognitive robotics because these words are far from perception and are perceived through the mind, not through the senses.. In this paper a cognitive robotic model is proposed to obtain abstract words semantic representation through indirect grounding in sensorimotor experience. The model employs the symbolic knowledge representation method (graph-based semantic network) for the robot’s conceptual knowledge. The robot perceives abstract words in this model by referring to already grounded concrete words with sensorimotor experience, so it is not subject to the symbol grounding problem. An algorithm is written to obtain semantic referents of abstract words through transitive inference method. A simulation experiment has been designed for DARwIn–OP robot model for verification. Keywords: Cognitive Robotics, Abstract Words Representation, Symbol Grounding Problem, Semantic Network, Grounded Cognition