Providing Advice on the Acquisition and Reuse Of Knowledge Bases in Problem Solving

Many tools and techniques have been developed for the systematic acquisition of domain knowledge, including knowledge elicitation (KE) methods to acquire knowledge from a human expert, machine learning (ML) algorithms that infer knowledge from data, and knowledge base refinement (KBR) tools that refine existing knowledge bases (KBs). As the number and sophistication of knowledge acquisition tools increases, it becomes progressively more difficult for users (notably domain experts) to choose between them for particular applications, especially when more than one is needed. We recognise the importance of driving this process by the epistemological requirements of the problem solver(s) which have been selected to solve a particular task. To support this approach, we introduce the MUSKRAT toolbox, which includes an advisory system coupled to several knowledge acquisition tools and problem solvers. Thi