Automated Knowledge Acquisition for Strategic Knowledge

Strategic knowledge is used by an agent to decide what action to perform next, where actions have consequences external to the agent. This article presents a computer-mediated method for acquiring strategic knowledge. The general knowledge acquisition problem and the special difficulties of acquiring strategic knowledge are analyzed in terms of representation mismatch: the difference between the form in which knowledge is available from the world and the form required for knowledge systems. ASK is an interactive knowledge acquisition tool that elicits strategic knowledge from people in the form of justifications for action choices and generates strategy rules that operationalize and generalize the expert's advice. The basic approach is demonstrated with a human–computer dialog in which ASK acquires strategic knowledge for medical diagnosis and treatment. The rationale for and consequences of specific design decisions in ASK are analyzed, and the scope of applicability and limitations of the approach are assessed. The paper concludes by discussing the contribution of knowledge representation to automated knowledge acquisition.     

Using intentional models for the interface design of multi-level systems

In this paper, it is argued that the design of computer interfaces for complex, multi-layered systems needs to take into account the differing intentional models that are held by different types of users of such systems, and that there is a strong correlation between the job roles of individuals and the level of abstraction of the mental models held by such users. An approach to the analysis and design of complex multi-layered systems based on the analysis of job roles to elicit such models is suggested and linked with other techniques of task analysis and object-oriented analysis and design. The methodology is illustrated with the interface analysis for an automatic environmental chemical analyser.