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The Autonomous Underwater Vehicle (AUV) is a fully automated robot submarine that is capable of maintaining a set of electronic sensors under the polar icecap. This function is primarily an issue of automated planning. The AUV is driven by three independent, and often conflicting, goals. These are mission, survival, and covertness. The plan that must be generated is essentially a route to achieve a mission that is acceptable to all three goals. The conflicting goals are implemented as independent expert systems that place constraints on the route taken. A higher level arbiter is used to help resolve conflicts in the situations where restraints posed by the independent goals preclude any solution being found.

Existing expert systems have a high percentage agreement with human experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the Integrated Diagnostic Model (IDM), that integrates two sources of knowledge: a shallow, empirically-oriented, experiential knowledge base and a deep, functionally-oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair of electrical and mechanical devices, two implementations and our experience with them is described.