Confirmatory Research on a Representative Method of Describing Knowledge Task Process

Huan Cao, Yongjian Li


This paper presents research findings that knowledge task process representation can be used to enhance the efficiency of knowledge tasking. This representative knowledge tasking can simulate knowledge tasking in the same way used in artificial intelligence and knowledge engineering.  It can describe the process of how to solve the Tower of Hanio in predicate logic, and analyze the problems that exist in knowledge representation. Based on cognitive science it can advance the approach to represent knowledge in Mind Operation (MO) by case study and use confirmatory factor analysis to validate the ubiquity of MO. The data supports the 9 element mind operation concept set. It further provides a basic concept and methodology to measure and optimize tasks, and explores a way of knowledge representation in AI.

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