A hypotheses-driven framework for human–machine expertise process

Abstract

The hypothesis-driven methodology is a cognitive activity used in expertise processes to solve problems with limited knowledge and understanding. Although some organizations have standardized this approach to guide humans in carrying out expertise in enterprises, it lacks appropriate tools to assist experts in carrying out this cognitive activity, tracking understanding, or capturing the reasoning steps and the knowledge produced during the process.

To acquire, share and reuse experts’ knowledge applied during expertise processes while assisting humans in bringing understanding to complex problems, this study introduces a human–machine collaborative framework that formalizes experts’ knowledge from the hypothesis-driven methodology described in the France standard NF X50-110 of “Quality of expertise activity”. This framework utilizes Hypothesis Theory extended with qualitative doubt and a systematic reasoning process to generate a hypothesis exploratory graph (HEG).

The proposed approach makes it easier to carry out expertise processes through a human–machine collaboration, offers a means to share and reuse knowledge from expertise, and provides expertise processes evaluation mechanisms. Furthermore, an experiment conducted on a use-case of expertise process verifies the feasibility and effectiveness of the approach.