ABSTRACT
In this paper, we present results from two empirical studies in which subjects diagnosed faults that occurred in a computer-based, dynamical simulation of an oil-fired marine power plant, called Turbinia. Our results were analyzed in the framework of dual problem space search (DPSS), in which non-routine diagnosis was characterized as a process of generating hypotheses to explain the observed faults, and testing these hypotheses by conducting experiments.
In the first study, we found that the less-efficient subjects conducted significantly more experiments, indicating a strong bottom-up bias in their diagnostic strategy. In the second study, we examined the effects of imposing external resource bounds on subjects’ diagnostic strategies. Results indicated that constraints on diagnosis time led to a reduction in the number of actions performed and components viewed, without appearing to affect diagnostic performance. Constraints on the number of diagnostic tests reduced search in the experiment space, which appeared to negatively affect performance. Taken together, these suggest results that subjects’ diagnostic strategies were sensitive to constraints in the external task environment. We close with a sketch of how DPSS might be augmented to include effects due to external resource bounds.
