Bidirectional Cognitive Computing Model for Uncertain Concepts

Most intelligent computing models are inspired by various human/natural/social intelligence mechanisms during the past 60 years. Achievements of cognitive science could give much inspiration to artificial intelligence. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. The human cognition has been researched in many fields. Some uncertain theories are briefly analyzed from the perspective of cognition based on concepts. In classical intelligent information systems, original data are collected from environment at first; usually, useful information is extracted through analyzing the input data then, it is used to solve some problem at last. There is a common characteristic between traditional machine learning, data mining, and knowledge discovery models. That is, knowledge is always transformation from data. From the point of view of granular computing, it is a unidirectional transformation from finer granularity to coarser granularity. Inspired by human’s granular thinking and the cognition law of “global precedence”, the human cognition process is from coarser granularity to finer granularity. Generally speaking, concepts (information and knowledge) in a higher granularity layer would be more uncertain than the ones in a lower granularity layer. A concept in a higher granularity layer would be the abstraction of some objects (data or concepts in a lower granularity layer). Obviously, there is a contradiction between the unidirectional transformation mechanism “from finer granularity to coarser granularity” of traditional intelligent information systems with the global precedence law of human cognition. That is, the human cognition are different the computer cognition for uncertain concept. The human cognition for knowledge (or concept) is based on the intension of concept, while the computing of computer (or machine) is based on the extension. In order to integrate the human cognition of “from coarser to finer” and the computer’s information processing of “from finer to coarser”, a new cognitive computing model, bidirectional cognitive computing model between the intension and extension of uncertain concepts, is proposed. The purpose of the paper is to establish the relationship between the human brain computing mode (computing based on intension of concept) and the machine computing mode (computing based on extension of concept) through the way of computation. The cloud model theory as a new cognition model for uncertainty proposed by Li in 1995 based on probability theory and fuzzy set theory, which provides a way to realize the bidirectional cognitive transformation between qualitative concept and quantitative data—forward cloud transformation and backward cloud transformation. Inspired by the cloud model theory, the realization of the bidirectional cognitive computing process in the proposed method is that the forward cloud transformation algorithm can be used to realize the cognitive transformation from intension to extension of concept, while the backward cloud transformation algorithm is to realize the cognitive transformation from extension to intension. In other words, the forward cloud transformation is a converter “from coarser to finer”, and the backward cloud transformation is a converter “from finer to coarser”. Taking some uncertain concepts as cognitive unit of simulation, several simulation experiments of the bidirectional cognition computing process are implemented in order to simulate the human cognitive process, such as cognition computing process for an uncertain concept with fixed samples, cognition computing process of dynamically giving examples, and cognition computing process of passing a concept among people. These experiment results show the validity and efficiency of the bidirectional cognitive computing model for cognition study.