The ideas of MTL and ITL have potential for being applied in a larger context as a model for conceptualizing the mix of organizational learning questions, i.e. applying the ideas of multistrategy learning at the planning level vs. the execution level. From the user's point of view, all of these are viewed simply as questions. KADS provides a somewhat different view of knowledge structures since it is not a product of machine learning, but rather grew out of attempts to provide generic task models to be used as templates and architectural guides to the construction of knowledge-based systems. It builds upon B. Chandrasekaran's work on the definition of generic task models, e.g. for diagnosis, configuration, and design tasks. Tasks relate to higher forms of questions, which are necessarily decomposed into lower level knowledge operations for execution purposes.
Tasks involve supporting decisions , which are in turn related to supporting questions.
Certain types of learning goals may be more easily described in terms of tasks and decisions vs. specific lower-level learning operations. There are many models of organizational learning proposed in the literature, including, e.g., those of Huber, Argyris, Kim, and Garvin. Data interpretation plays the central role in the model by creating knowledge from environmental data, and by linking the knowledge to the support of organizational goals, decisions and actions. Nonaka draws on experiences in Japanese firms, which tend to respect tacit and intuitive forms of human knowledge. In addition to knowledge creation, the model explicitly addresses knowledge communication and distribution, and transfer of knowledge between individuals and groups.