Acquisition of abstract words for cognitive robots

Abstract

Abstract word learning and comprehension is a very crucial and important issue because of its application and problematic nature. This problem does not just belong to the cognitive robotics field, as it also has significance in neuroscience and cognitive science. There are many issues like symbol grounding problem and sensory motor processing within grounded cognition framework and conceptual knowledge representation methods that have to be addressed and solved for the acquisition of abstract words in cognitive robots. This paper explains these concepts and matters, and also elucidates how these are linked to this problem. In this paper, first symbol grounding problem is discussed, and after that an overview of grounded cognition be given along with detail of methods/ideas that suggest how abstract word representation could use sensory motor system. Finally, the computation methods used for the representation of conceptual knowledge are discussed. Two cognitive robotics models based on Neural network and Semantic network that ground abstract words are presented and compared via simulation experiment to find out the pros and cons of computation methods for this problem. The aim of this paper is to explore the building blocks of cognitive robotics model at theoretical and experimental level, for grounding of abstract words.