Human error is a major factor in failures across safety-critical domains. Such failures are very complex, with human errors often arising as a result of unexpected interactions between system automation and human behavior. Thus, researchers have investigated how formal methods tools and techniques, which have been developed to mathematically prove properties about complex computer systems, can be adapted to human-automation interaction (HAI) problems. These techniques are powerful and capable of discovering unexpected, critical human errors and system failures. However, existing techniques do not provide a means for fixing discovered human errors. Further, interface changes both introduce new unforeseen errors and risk negative transfer effects, where changes that conflict with previously learned behaviors can also cause problems. This project will investigate a novel approach to HAI evaluation and repair that will help designers and analysts efficiently eliminate many kinds of potential interaction errors while minimizing the risk of introducing additional human errors. The developed methods will be validated in design cases of real safety-critical systems including an industrial furnace, nuclear power plant procedures, a radiation therapy machine, and pharmacy medication dispensing processes. The knowledge and tools produced in this research will be made available to researchers and designers and have potential applications to a wide range of many safety-critical systems. This, in turn, will help avoid system disasters, prevent injuries, save lives, and protect critical resources across society.
The project is divided into three main thrusts. First, the team will develop a theoretically grounded method for scoring the likelihood that humans will behave erroneously for a given HAI design through a novel synthesis of formal methods, erroneous human behavior models, negative transfer theory, and human reliability analyses. Second, it will introduce a new theory of formal model repair in interactive systems that will underlie the development of methods for removing problematic HAI errors by adapting both human-machine interfaces and the workflow of the associated tasks. Third, the scoring and model repair methods will be combined to allow automated model repair to find design interventions that will reduce the likelihood of changes causing problematic human errors, using a database of common error patterns and solutions to be developed through the project. Across all three of these thrusts, the team will use human subject experiments, testing, and formal proofs to validate that the advances achieve their hypothesized capabilities. The work will lead to improved methods for evaluating human reliability aspects of interfaces, widen the application of formal methods to new contexts, and provide resources for researchers, designers, and engineers to improve the reliability of cyber-human systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.