Diagnostic Problem Solving via Planning with Ontic and Epistemic Goals

ABSTRACT. Diagnostic problem solving involves a myriad of reasoning tasks associated with the determination of diagnoses, the generation and execution of tests to discriminate diagnoses, and the determination and execution of actions to alleviate symptoms and/or their root causes. Fundamental to diagnostic problem solving is the need to reason about action and change. In this work we explore these myriad of reasoning tasks through the lens of AI automated planning. We characterize a diversity of reasoning tasks associated with diagnostic problem solving, prove properties these characterizations, and define correspondences with established automated planning tasks and existing state-of-the-art planning systems. In doing so, we provide deeper insight into the computational challenges associated with diagnostic problems solving, as well as proposing practical algorithms for their realization.