. 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...

Models should be able to reproduce the known behaviour of whatever it is they are trying to model. In its most general form, this test is abduction; i.e. the generating an internally-consistent scenario that entails some subset of known observations given certain inputs. Exhaustive abduction (EA) is the generation of all such scenarios. EA can be used to verify a model. If all of the known behaviour cannot be found in any of the generated scenarios, then the model must be faulty. Given that abduction is known to be slow, a reasonable preexperimental intuition is that EA would not be a practical technique for large models. In the study presented here, EAs were executed for a variety of models of different sizes and internal fan-outs. The limits of EA for the current implementation and the studied models implied that EA has some practical utility as a validation tool. Keywords: validation, abduction, hypothesis testing, qualitative reasoning, neuroendocrinology. 1. INTRODUCTION Models...

Currently, "task analysis" is the dominant paradigm in the knowledge acquisition community. We argue that for performance systems (i.e. systems that do not have to offer a knowledge-level description of their performance at runtime) a simpler "test analysis" approach may suffice. We offer examples were a seemingly-naive testing regime gives rise to competent performance systems. Further, by certain measures, these systems developed via test analysis out-performed systems developed for similar domains using other techniques. Test analysis did not augment some other methodological approach: it removed the need for any other methodology. We speculate that for performance systems, task analysis could be deferred till after the development of a tested performance system. That is, for performance systems, testing replaced task but task analysis could augment test analysis once a system was in production. 1. INTRODUCTION Task analysis evolved from a reverse engineering of existing expert sy...