I have developed a methodology for knowledge representation and reasoning for agents working in exploratory-corrective domains. Working within the field of Artificial Intelligence in Medicine, I used the specific problem of diagnosis-and-repair in multiple trauma management as both motivation and testbed for my work. A reasoning architecture is proposed in which specialized diagnostic reasoning and planning components are integrated in a cycle of reasoning and action/perception: 1. A Goal-Directed Diagnostic (GDD) reasoner which is predicated on the view that diagnosis is only worthwhile to the extent that it can affect repair decisions and that goals can be used to focus on such. Rather than focusing on a diagnosis object as the primary purpose of the diagnostic process, the GDD reasoner is tasked primarily with generating goals for the planner and with reasoning about whether these goals have been satisfied. 2. A Progressive Horizon Planner (PHP) which works by constructing intermediate plans via a combination of plan sketching and selection/optimization sub-processes, and then adapting these plans to reflect new information and goals. For the plan sketching sub-part, I propose a selection-and-ordering planning/scheduling paradigm, taking advantage of the limited interaction between goals. I have implemented this architecture and reasoning components in TraumAID 2.0 - a consultation system for the trauma management domain. In a blinded comparison, out of 97 real trauma cases, three trauma surgeons have judged management plans proposed by TraumAID 2.0 preferable to the actual care by a ratio of 64:17 and to plans generated by its predecessor TraumAID 1.0 by a ratio of 62:9.