In this paper, we provide a taxonomy of the processes which people use to generate questions for a type of interviewing task. Specifically, we analyze "story acquisition interviews" in which the interviewer is a knowledge engineer who asks questions of a domain expert to acquire material for a conversational hypermedia system. Such interviews have proven to be surprisingly difficult to conduct successfully. W e have identified a number of "local" strategies which successful interviewers use to develop coherent, interesting sequences of questions and we have positioned these strategies within a model which describes the global interviewing process. This descriptive model is an initial step towards a methodology prescribing how to perform these interviews effectively.

When solving a complex problem, gathering relevant information to understand the situation and imposing appropriate interpretations on that information are critical to problem solving success. These two tasks are especially difficult in weak-theory domains -- domains in which knowledge is incomplete, uncertain, and contradictory. In such domains, experts may rely on experience for all aspects of problem solving. We have developed a case-based approach to problem elaboration and interpretation in such domains. An experience-based problem-solver should be able to incrementally acquire information and, in the course of that acquisition, be reminded of multiple cases in order to present multiple viewpoints to problems that present multiple faults. We are addressing issues of 1) elaboration and interpretation of complex problem situations; 2) multiple interpretations; and 3) the role of categories as the foci of reasoning in the context of the Organizational Change Advisor (ORCA). Its model of incremental reminding is a plausible mechanism for this sort of expert problem solving behavior, and one that works well in weak theory domains. Because there is an implicit cost associated with retrieving a complex case, O R C A implements a retrieval time similarity function that requires both general expectations and specific situational relevance be considered before a story is told to the user; this increases the chances that a retrieved case will be useful.

This paper describes a memory organization that supports intelligent memory-based argumentation. Our goal is to build a system that can argue opposite sides of an issue by retrieving stories that support or oppose it. Rather than attempting to determine how a story relates to a point on the fly, we explicitly represent the points that the stories support or oppose, as well cis how they support or oppose those points. We have developed a hierarchy of story point types; associated with each type is a set of rhetorical templates, which describe the ways that a story could support or oppose a point of that type. Each template consists of a series of assertion types on which the argument depends. This enables the program to attack intelligently the foundations of the point it is trying to refute. Our approach is being developed within the context of the ILS Story Archive, a large multimedia case base which includes stories from a wide variety of domains.

Research in case-based design has demonstrated some capability to retrieve relevant designs and to adapt them automatically to satisfy new design constraints. However, some domains are less amenable to automated adaptation, particularly when the cases are very complex and when relationships among the design components are difficult to express formally. The design of interactive learning environments is one such domain. W e describe a case-based approach to instructional software design which utilizes interactive, model-driven case adaptation. Our model for computer-based instruction is Goal-Based Scenarios. W e describe a tool, Goal-Based Scenario Builder, which supports interactive adaptation of instructional software using the model, and illustrate its use in adapting an example case of a successful instructional software program, Sickle Cell Counselor.