Graded Concepts for Collaborative Intelligence

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.