A Novel Machine Learning Algorithm for Cognitive Concept Elicitation by Cognitive Robots

Yingxu Wang and Omar A. Zatarain
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Yingxu Wang: International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Omar A. Zatarain: International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 3, 31-46

Abstract: Cognitive knowledge learning (CKL) is a fundamental methodology for cognitive robots and machine learning. Traditional technologies for machine learning deal with object identification, cluster classification, pattern recognition, functional regression and behavior acquisition. A new category of CKL is presented in this paper embodied by the Algorithm of Cognitive Concept Elicitation (ACCE). Formal concepts are autonomously generated based on collective intension (attributes) and extension (objects) elicited from informal descriptions in dictionaries. A system of formal concept generation by cognitive robots is implemented based on the ACCE algorithm. Experiments on machine learning for knowledge acquisition reveal that a cognitive robot is able to learn synergized concepts in human knowledge in order to build its own knowledge base. The machine–generated knowledge base demonstrates that the ACCE algorithm can outperform human knowledge expressions in terms of relevance, accuracy, quantification and cohesiveness.

Date: 2017
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