Grounding explanations in evolving, diagnostic situations

This research was motivated by the desire to further understanding on how artificial intelligence (AI) systems may effectively support practitioners engaged in fault management in dynamic situations. Standard approaches to diagnostic assistance in which retrospective explanations are provided are not well suited to the demands of dynamic fault management. Such explanations occur as interruptions in the flow of work and result in data overload. A field study of human practitioners in one dynamic fault management application (anesthesiologists during neurosurgical operations) was undertaken in order to gain insight into effective diagnostic support among team members. The conceptual framework that guided the field study drew from research on cooperation in communication, and particularly on work from conversation analysis on the "common ground" maintained during coordinative activity.;The findings indicate that team members assist one another in maintaining accurate interpretations of the process by helping one another keep track of influences on the process. Two ways they do this are by providing unprompted reports of their relevant activities on the process and by providing informative responses that go beyond answering explicitly posed questions. Episodes of management and diagnosis show that causal explanations among team members are better described as joint interpretations (in which both team members are involved in the process of attaining a mutual interpretation), rather than as retrospective explanations given from one team member to another. Explanations of assessments and activities that are found are typically brief and in the flow of activity.;The general implications for the design of intelligent systems intended to support practitioners in dynamic fault management are that such systems should not be "dark boards" concerning their activities and assessments. But, because they lack many of the sophisticated capabilities displayed in human communication, intelligent system design must avoid distracting in an effort to maintain the common ground. Instead, the focus should be on providing intelligent system assessments and information about activities in the context of (i.e., relative) to events in the dynamic process.