Generating explanations of device behavior is a long-standing goal of AI research in reasoning about physical systems. Much of the relevant work has concentrated on new methods for modeling and simulation, such as qualitative physics, or on sophisticated natural language generation, in which the device models are specially crafted for explanatory purposes. We show how two techniques from the modeling research---compositional modeling and causal ordering ---can be effectively combined to generate natural language explanations of device behavior from engineering models. The explanations offer three advances over the data displays produced by conventional simulation software: (1) causal interpretations of the data, (2) summaries at appropri ate levels of abstraction (physical mechanisms and component operating modes), and (3) query-driven, natural language summaries. Furthermore, combining the compositional modeling and causal ordering techniques allows models that are mo...

. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried out. These methods are concerned with classifying the different types of problem solving and providing tools and methods to help the knowledge engineer identify the appropriate approach and ensure nothing is omitted.. A different approach to knowledge acquisition focuses on ensuring incremental addition of validated knowledge as mistakes are discovered (validated knowledge here means only that the earlier performance of the system is not degraded by the addition of new knowledge). The organisation of this knowledge is managed by the system rather than the expert and knowledge engineer. This would seem to correspond to human incremental development of expertise. From this perspective...