Graded Concepts for Collaborative Intelligence

T. Martin; B. Azvine

doi:10.1109/SMC.2018.00443

Graded Concepts for Collaborative Intelligence

T. Martin, B. Azvine

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

214 Downloads (Pure)

Abstract

Collaborative intelligence involves a combination of human and machine-based analysis, in which humans focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. We have proposed the use of graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. Graded concepts allow summarization at multiple levels of discernibility (granularity). In this paper, we outline a new interpretation of fuzzy concept lattices as graded sets of crisp lattices. In addition, we prove equivalence between graded (fuzzy) formal concept analysis and the standard crisp framework. Consequently, any software tools developed for crisp data can be extended to the graded case without change.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2589-2594
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00443
Publication status	Published - 16 Jan 2019
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher	IEEE
ISSN (Print)	1062-922X

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

Bibliographical note

Export Date: 5 July 2019

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10.1109/SMC.2018.00443

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This is the accepted author manuscript (AAM). The final published version (version of record) is available online via OSA at https://doi.org/10.1364/OL.44.000275 . Please refer to any applicable terms of use of the publisher.
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Martin, T., & Azvine, B. (2019). Graded Concepts for Collaborative Intelligence. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2589-2594). [8616439] (IEEE International Conference on Systems, Man, and Cybernetics (SMC)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SMC.2018.00443

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Martin, T & Azvine, B 2019, Graded Concepts for Collaborative Intelligence. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616439, IEEE International Conference on Systems, Man, and Cybernetics (SMC), Institute of Electrical and Electronics Engineers (IEEE), pp. 2589-2594, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00443

Graded Concepts for Collaborative Intelligence. / Martin, T.; Azvine, B.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 2589-2594 8616439 (IEEE International Conference on Systems, Man, and Cybernetics (SMC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AB - Collaborative intelligence involves a combination of human and machine-based analysis, in which humans focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. We have proposed the use of graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. Graded concepts allow summarization at multiple levels of discernibility (granularity). In this paper, we outline a new interpretation of fuzzy concept lattices as graded sets of crisp lattices. In addition, we prove equivalence between graded (fuzzy) formal concept analysis and the standard crisp framework. Consequently, any software tools developed for crisp data can be extended to the graded case without change.

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Graded Concepts for Collaborative Intelligence

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