부산대학교 도서관

Formal human-machine analyses based around human mental models have shown great utility for discovering when and how people may develop mode confusion and be surprised or disoriented by automated behavior. Such analyses represent mental models with finite state machine formalisms. These are limited in that they assume unrealistic precision in how people thing about states and input events. This paper proposing a new formalism called Fuzzy Mental Model Finite State Machines (FMMFSMs). FMMFSMs combine state machine modeling with fuzzy logic to allow for precise reasoning about the imprecision of human mental model states and inputs. This has the potential to enable formal mental model analyses to support traditional mode confusion, but also account for things like drift: where the humans mental model changes over time due to stagnant or slowly changing conditions. This paper presents the FMMFSMs formalism and illustrates is potential for finding mode confusion, automation surprise, and trust disruption with an automobile automation application. Implications of these developments and future research are discussed.