I-Know: Knowledge Representation and Manipulation for Self-aware Robots

Abstract

Knowledge representation is one of the crucial tasks while designing an artificially intelligent/self-aware agents (softbots or robots). Construction of knowledge-base and its use has been seen in past practices but with the advancement and involvement of a new generation of robotics where robot collaborate with human or with other robots. There is a requirement of knowledge-base which are not hand-crafted and the knowledge is acquired from the sensory modality interpret it and store the knowledge. The advancement in artificial intelligence (AI) design system requires explicit knowledge with the implicit knowledge. In order to extract structured knowledge from unstructured information, different information extraction techniques have been introduced. Moreover, the artificially intelligent systems are base on different cognitive architectures. These architectures at the ground level follow the same basic philosophies of cognition and regardless of school of thought the knowledge is represented using the state-of-the-art representation scheme. The utilization of these schemes are modified according to the requirement and philosophical structure of the cognitive architecture. This difference blew up the limitation of compatibility if one wants to integrate the representation scheme with other such as if representation scheme of CLARION is needed to be extended with the design rationale of atomspace they are incompatible with each other. In order to resolve this, issue this study proposed a framework for unified representation scheme which takes the best properties of the existing architecture and represents them into the atom of knowledge. The proposed representation scheme is designed to make it compatible with both softbots and robots