John H. Boose’s research while affiliated with CIATEQ and other places

Acquiring knowledge from a human expert is a major problem when building a knowledge-based system. Aquinas, an expanded version of the Expertise Transfer System (ETS), is a knowledge-acquisition workbench that combines ideas from psychology and knowledge-based systems research to support knowledge-acquisition tasks. These tasks include eliciting distinctions, decomposing problems, combining uncertain information, incremental testing, integration of data types, automatic expansion and refinement of the knowledge base, use of multiple sources of knowledge and providing process guidance. Aquinas interviews experts and helps them analyze, test, and refine the knowledge base. Expertise from multiple experts or other knowledge sources can be represented and used separately or combined. Results from user consultations are derived from information propagated through hierarchies. Aquinas delivers knowledge by creating knowledge bases for several different expert-system shells. Help is given to the expert by a dialog manager that embodies knowledge-acquisition heuristics. Aquinas contains many techniques and tools for knowledge acquisition; the techniques combine to make it a powerful testbed for rapidly prototyping portions of many kinds of complex knowledge-based systems.

Creation and maintenance of links in large hypermedia documents are difficult. Motivated by an application to a federal clinical practice guideline for cancer pain management, we have developed and evaluated a repertory grid-based linking scheme we call repertory hypergrids. Harnessing established knowledge acquisition techniques, the repertory hypergrid assigns each "knowledge chunk" a location in "context space". A chunk links to another chunk if they are both close in context space. We have developed a program to convert the hypergrid and associated knowledge chunks to HTML and have made the hypermedia clinical practice guideline available on the World Wide Web.To evaluate the scheme, we conducted two analyses. First, we conducted a protocol analysis using the paper-based guidelines. Six users of the guideline addressing typical cancer pain management tasks made 30 explicit links. The repertory hypergrid using a neighborhood size of 16 captures of 24 of these links. With optimization, the repertory hypergrid captures 27 of the links with a neighborhood size of 14. Second, 18 users addressed the same tasks, six using the paper-based guideline, six using the hypermedia document with repertory hypergrid-created links ("TALARIA"), and six using the hypermedia document with randomly selected links ("Random TALARIA"). TALARIA users found the required information significantly more quickly than either the users of the paper-based guideline or of Random TALARIA, with no loss in accuracy.

This paper introduces a panel to be held at the Knowledge Acquisition Track of the Machine Learning Workshop (ML91). This panel will focus on the problem of acquiring design rationale knowledge from humans for later reuse. The design of tools for design rationale capture reveals several fundamental issues for knowledge acquisition, such as the relationships among formality and expressiveness of representations, and kinds of automated support for elicitation and analysis of knowledge. This paper sets the background for discussion by identifying dimensions of a design space for design rationale tools, and then includes position statements from each panelist arguing for various positions in this space. 1 THE PROBLEM OF DESIGN RATIONALE CAPTURE For many engineering tasks, including redesign, verification, and diagnosis, it is important to know why an artifact was designed the way it was. Design rationale is a general term referring to the knowledge or reasoning underlying...

Creation and maintenance of links in large hypermedia documents is difficult. Motivated by an application to a federal clinical practice guideline for cancer pain management, we have developed and evaluated a repertory grid-based linking scheme we call repertory hypergrids. Harnessing established knowledge acquisition techniques, the repertory hypergrid assigns each “knowledge chunk” a location in “context space”. A chunk links to another chunk if they are both close in context space.To evaluate the scheme, we conducted a protocol analysis. Six users of the guideline addressing typical cancer pain management tasks made 30 explicit links. The repertory hypergrid using a neighborhood size of 16 captures 24 of these links. With optimization, the repertory hypergrid captures 27 of the links with a neighborhood size of 13.

Existing group decision support systems used in meeting rooms can help teams reach decisions quickly and efficiently. However, the decision models used by these systems are inadequate for many types of problems. This paper describes our laboratory's experience with knowledge acquisition systems and decision support tools. Our studies led us to develop a comprehensive decision model for group decision support systems. This decision model combines current brainstorming-oriented methods, structured text argumentation (using the gIBIS model), repertory grids, possibility tables (morphological charts) and influence diagrams from decision analysis. Each component addresses weaknesses in current group decision support systems. We are assembling these group decision support components together into a group decision workbench.

Personal construct theory provides both a plausible theoretical foundation for knowledge acquisition and a practical approach to modeling. Yet, only a fraction of the ideas latent in this theory have been tapped. Recently, several researchers have been taking a second look at the theory, to discover new ways that it can shed light on the foundations and practice of knowledge acquisition. These efforts have led to the development of three "second-generation" constructivist knowledge acquisition systems: DDUCKS, ICONKAT, and KSSn/KRS. These tools extend repertory grid techniques in various ways and integrate them with tools springing from complementary perspectives. New understandings of relationships between personal construct theory, assimilation theory, logic, semantic networks, and decision analysis have formed the underpinnings of these systems. Theoretical progress has fostered practical development in system architecture, analysis and induction techniques, and group use of knowledge acquisition tools.