Boose/Bradshaw Knowledge Acquisition Reports

The following reports from the Boose and Bradshaw knowledge acquisition research group at Boeing Computer Services have been posted to the web at the request of John Boose.

Abstracts and links to the reports in Microsoft Word format are listed below. The abstracts themselves are also available as a Word file.

Beyond the Repertory Grid: New Approaches to Constructivist Knowledge Acquisition Tool Development, Jeffrey M. Bradshaw, Kenneth M. Ford, Jack R. Adams-Webber, John H. Boose, 1993, Wiley; In K. M. Ford & J. M. Bradshaw (Ed.), Knowledge Acquisition as Modeling.

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.

Canard: An Alternative Generation Tool Based On Possibility Tables, Jeffrey M. Bradshaw, David Shema, John H. Boose & Joseph L. Koszarek, 1993, In S. Kim (Ed.), Creativity: Models, Methods, and Tools. AAAI Press.

Knowledge acquisition and modeling tools can play an important role in supporting creative processes, alternately encouraging divergence through mechanisms such as mutation and constraint relaxation, and facilitating convergence through structuring, reasoning, and optimization mechanisms. Because of the fluid nature of the design space, it is important that knowledge modeling systems supporting design be highly visual and incremental. Canard is a tool for alternative generation that aims to support visual, incremental exploration of design space. Using possibility tables, designers identify the components of an acceptable design, specify possibilities for each component, develop criteria reflecting preferences among possibilities, and supply constraints governing compatibility between components and overall design considerations. Designers can interactively explore design alternatives by selecting possibilities for each component, modifying or adding components and possibilities as they gain insight about the design space. An iterative search procedure that proposes new alternatives based on permutations of the constraint space assists in generation of alternatives. Through an analogous procedure, the system can hypothesize new constraints based on examples of previously defined alternatives. We examples that show the use of Canard for configuration and design activities.

Cyc Evaluation: Is MCC Breaking the Knowledge Barrier?, Patricia Riddle, John Boose, Heather Holmback, William Jones, Aki Namioka, John Thompson

This report, completed in mid-1992, evaluates Cyc's potential usefulness at Boeing.

A flexible six-step program for defining and handling bias in knowledge elicitation, Mary A. Meyer, Jane M. Booker, Jeffrey M. Bradshaw

While concern with the quality of expert knowledge is sensitizing knowledge engineers to the issue of bias, little guidance yet exists on how to recognize or handle its occurrence in knowledge acquisition. Bias is regarded here as a skewing of the data from some reference point. If the reference point is mathematical or logical rules, then biased data violates these rules, such as occurs with cognitive bias. If the reference point is the expert's expressions of his thinking, biased data are distortions of these expressions, such as in the case of motivational biases. In this paper, we provide information on how to select a definition of bias (cognitive or motivational) and how to choose a combination of steps for handling bias in a particular application. The steps are (1) anticipate which biases are likely to occur in the planned elicitation; (2) redesign the elicitation to make it less prone to these biases; (3) make the experts aware of the potential intrusion of these biases and familiarize them with the elicitation procedures; (4) monitor the elicitation for the occurrence of biases; (5) adjust, in real time, to correct for their occurrence; and (6) analyze the data to determine if they occurred. The program includes detailed descriptions of how to perform each of these steps using selected biases as examples.

eQuality: An Application of DDUCKS to Process Management, Jeffrey M. Bradshaw, Peter Holm, Oscar Kipersztok & Thomas Nguyen, In T. Wetter, K.-D. Althoff, J. H. Boose, B. R. Gaines, M. Linster, & F. Schmalhofer (Ed.), Current Developments in Knowledge Acquisition: EKAW-92.

Process management is a method for improving Boeing's business processes, however many aspects have been difficult to implement. eQuality is a software system based on a framework called DDUCKS that is being designed to support the process management life cycle. We take a knowledge acquisition approach to the development of the tool, emphasizing the importance of mediating and intermediate knowledge representations. Sharing and reuse of tools, models, and representations is facilitated through a layered architecture. eQuality's process documentation capability includes a number of views, that can be used either in sketchpad or model mode. Using the views, an integrated business enterprise model may be developed. Analysis and simulation tools supporting process improvement are implemented with attribute, function, and task editors that make use of a user scripting language and extensible function library. A virtual project notebook is used to organize project information and help facilitate group meetings.

KS-3000: An Application of DDUCKS to Bone-Marrow Transplant Patient Support, Jeffrey M. Bradshaw, C. Richard Chapman, Keith M. Sullivan, John H. Boose, Debra Zarley, Janet Nims, Jonathan Gavrin, Nigel Bush, Russell G. Almond, David Madigan, 1993, Bradshaw et al. Proceedings of the Seventh European Knowledge Acquisition for Knowledge-Based Systems Workshop (EKAW-93), Toulouse and Caylus, France

At the Fred Hutchinson Cancer Research Center (FHCRC), we are developing technology to support research nurses staffing a telephone consultation service for long-term bone-marrow transplant follow-up care. In this paper, we describe the design of KS-3000, an application of the DDUCKS development environment that will combine techniques from knowledge-based systems with those of decision analysis. KS-3000 provides a virtual notebook as the primary interface to the nurses. Knowledge acquisition and consultation tools are represented as pages in the notebook. Underlying knowledge structures are are represented using an enhanced version of Skuce and Lethbridge's CODE4 system. A probabilistic inference engine is used to make patient predictions or recommendations, while a Bayesian statistical learning component exploits accumulating patient data to continously improve the quality of these predictions. Layering of domain knowledge and problem-solving techniques and steps in knowledge-based construction of the probabilistic model are described. Finally, the project plan and evaluation study design are outlined.

New Directions for Computer-Based Training and Performance Support in Aerospace, Jeffrey M. Bradshaw, Tyde Richards, Peter Fairweather, Chuck Buchanan, Roger Guay, David Madigan, Guy A. Boy

This paper describes the purpose and current activities of a new emerging technologies and concepts (etc.) subcommittee of the Aviation Industry Computer-Based Training Committee (AICC). The objective of the etc. subcommittee is to help computer-based training (CBT) software and hardware suppliers, developers, and users in aerospace to better respond to important industry problems, including the increasing importance of standards efforts, the accelerating pace of technological change, and reductions in CBT budgets and resources. The subcommittee sponsors pre-competitive cross-industry Sisyphus projects that develop and evaluate prototypes of new technology. In this paper, we describe three interdependent studies currently being undertaken: Mercury, an effort to evaluate and apply emerging platform-independent multimedia and information standards; Pegasus, an application of new methods of "authoring for reuse" in different kinds of performance support settings; and Hippocrene, an investigation of adaptive performance support using intentional agents.

Expertise Transfer for Expert System Design, Boose, J.H. (1986) text of book, Elsevier.

This book is about the Expertise Transfer System (ETS). ETS interviews experts to help them build expert systems. The interviewing methodology is based on techniques from personal construct theory that are used in psychotherapy.

Expertise Transfer and Complex Problems: Using AQUINAS as a Knowledge Acquisition Workbench for Knowledge-Based Systems, John H. Boose and Jeffrey M. Bradshaw, 1987, International Journal of Man-Machine Studies, Vol. 26, No.1.

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.

REFINING PROBLEM-SOLVING KNOWLEDGE IN REPERTORY GRIDS USING A CONSULTATION MECHANISM, David B. Shema and John H. Boose, 1988, International Journal of Man-Machine Studies, Vol. 29, No. 4

A general problem when modifying knowledge bases is that changes may degrade system performance. This is especially a problem when the knowledge base is large; it may be unclear how changing one item in a knowledge base containing thousands of items will affect overall system performance. Aquinas, a knowledge acquisition tool, uses knowledge elicitation and representation techniques and consultation review mechanisms to help alleviate this problem. The consultation review mechanisms are discussed here. We are experimenting with ways to use consultations and test cases to refine the information in an Aquinas knowledge base. The domain expert can use interactive graphics to specify the expected results. Modifications to the knowledge base may be tested against previous consultations; adjustments are suggested that make the results of all previous consultations as well as the current consultation correlate better with the expert's expectations.  New traits are synthesized that would improve the performance of all previous consultations. New test cases are suggested that cover aspects missed by previous test cases. While we are just beginning to experiment with these techniques, they promise to provide help in improving problem-solving performance and gaining problem-solving insight.

Decision Analysis Techniques for Knowledge Acquisition: Combining Information and Preferences using Aquinas, Jeffrey M. Bradshaw and John H. Boose, 1990 International Journal of Man-Machine Studies, Vol. 32, No. 2

The field of decision analysis is concerned with the application of formal theories of probability and utility to the guidance of action. Decision analysis has been used for many years as a way to gain insight regarding decisions that involve significant amounts of uncertain information and complex preference issues, but it has been largely overlooked by knowledge-based system researchers. This paper illustrates the value of incorporating decision analysis insights and techniques into the knowledge acquisition and decision making process. This approach is being implemented within Aquinas, an automated knowledge acquisition and decision support tool based on personal construct theory that is under development at Boeing Computer Services. The need for explicit preference models in knowledge-based systems will be shown. The modeling of problems will be viewed from the perspectives of decision analysis and personal construct theory. We will outline the approach of Aquinas and then present an example that illustrates how preferences can be used to guide the knowledge acquisition process and the selection of alternatives in decision making. Techniques for combining supervised and unsupervised inductive learning from data with expert judgment, and integration of knowledge and inference methods at varying levels of precision will be presented. Personal construct theory and decision theory are shown to be complementary: the former provides a plausible account of the dynamics of model formulation and revision, while the latter provides a consistent framework for model evaluation. Applied personal construct theory (in the form of tools such as Aquinas) and applied decision theory (in the form of decision analysis) are moving along convergent paths. We see the approach in this paper as the first step toward a full integration of insights from the two disciplines and their respective repertory grid and influence diagram representations.

Using Personal Construct Techniques for Collaborative Evaluation, Douglas Schuler, Peter Russo, John Boose, and Jeffrey Bradshaw, 1990 International Journal of Man-Machine Studies, v.33

Efforts are underway to better characterize group processes at The Boeing Company as part of a project to design software for computer supported collaboration. This paper describes work in progress to support multi-user, collaborative situations using Aquinas, a knowledge acquisition workbench. An experiment is described in which Aquinas is used to facilitate the collaborative evaluation of an in-house Boeing Advanced Technology Center course in knowledge engineering.


Repertory grid-centered knowledge acquisition tools are useful as knowledge engineering aids when building many kinds of complex knowledge-based systems.These systems help in rapid prototyping and knowledge base analysis, refinement, testing, and delivery. These tools, however, are also being used as more general knowledge-based decision aids. Such features as the ability to very rapidly prototype knowledge bases for one-shot decisions and quickly combine and weigh various sources of knowledge make these tools valuable outside of the traditional knowledge engineering process. This paper discusses the use of repertory grid-centered tools such as the Expertise Transfer System (ETS), Aquinas,Kitten, and KSS0. Dimensions of use are presented along with specific applications. Many of these dimensions are discussed within the context of ETS and Aquinas applications at Boeing.

Knowledge Acquisition Tools, Methods, and Mediating Representations, John H. Boose, 1990, in John H. Boose. in Motoda, H., Mizoguchi, R., Boose, J. H., and Gaines, B. R. (Eds.) (1990). Proceedings of the First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop: JKAW-90, Ohmsha, Japan

This paper discusses knowledge acquisition problems, mediating representations, and contains an updated catalog of current knowledge acquisition tools and techniques.

JKAW90 Summary

The first Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop was held in October, 1990. It was tenth in an international series of knowledge acquisition workshops and was the first one held in the new Pacific Rim series. The workshop consisted of two open days held at the Kyoto International Conference Hall on October 25 - 26, and three closed days held at Hitachi Ltd.'s Advanced Research Laboratory in Tokyo on October 29 - 31. This report comtains summaries of the open days and a listing of the proceedings.

Design Knowledge Capture and Alternatives Generation Using Possibility Tables in Canard, David B. Shema, Jeffrey M. Bradshaw, Stanley P. Covington, and John H. Boose, 1990 Knowledge Acquisition Journal, 2(4)

During the evolution of a design concept, designers must integrate diverse sources and kinds of information about requirements, constraints, and tradeoffs. In doing so, they make certain assumptions and develop criteria against which alternatives are evaluated for suitability. Unfortunately, much of this process is implicit, making later review difficult if not impossible. When requirements change, impacts on the design are difficult to trace. This can lead to costly rework or serious errors. We are developing Canard, an automated tool which uses possibility tables, constraints, and knowledge bases to assist in the generation of design alternatives consistent with goals and constraints. The facility also attempts to capture and document assumptions and tradeoffs made during the design process. We present an example which illustrates the use of Canard for a simple configuration problem. A more complex example traces the activity of a Boeing expert building a possibility table for robot arm design. Finally, the application of Canard to a NASA corporate memory facility project is described.

How To Do With Grids What People Say You Can't, Jeffrey M. Bradshaw, John H. Boose, Stanley P. Covington, and Peter J. Russo, 1989 Knowledge Acquisition Journal Vol.1 No.4 blockquote>Although the representation of knowledge in grids is convenient for many purposes, there are still some significant limitations. One of the most challenging tasks still remaining is to evaluate the use of grids and personal construct methodology in problems that require constructive alternative generation and access to external tools and databases (i.e., synthesis as opposed to analysis problems). In this paper, we describe three components of an approach in support of this objective: 1. Definition of grid-based methods for interactive alternative generation and constraint discovery; 2. Development of an "open architecture" knowledge acquisition and decision support environment that supports asynchronous communication with external procedures and applications; and 3. Development of facilities for more sophisticated control and guidance of during knowledge acquisition and inference. These components are being implemented in la folie deux (FAD), an environment that integrates components from Aquinas, a knowledge acquisition workbench based on personal construct theory, with those of Axotl, a knowledge-based decision analysis workbench.

Knowledge Acquisition Techniques for Group Decision Support, John H. Boose, Jeffrey M. Bradshaw, Joseph L. Koszarek, David B. Shema, 1993 Knowledge Acquisition Journal, Vol. 5, No. 4

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 lead 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.

A SURVEY OF KNOWLEDGE ACQUISITION TECHNIQUES AND TOOLS, John H. Boose, 1989 Knowledge Acquisition Journal 1(1)

Knowledge acquisition tools can be associated with knowledge-based application problems and problem-solving methods. This descriptive approach provides a framework for analyzing and comparing tools and techniques, and focuses the task of building knowledge-based systems on the knowledge acquisition process. Knowledge acquisition research strategies discussed at recent Knowledge Acquisition Workshops are shown, distinguishing dimensions of knowledge acquisition tools are listed, and short descriptions of current techniques and tools are given.

RECENT PROGRESS IN AQUINAS: A KNOWLEDGE ACQUISITION WORKBENCH, John H. Boose, David B. Shema, Jeffrey M. Bradshaw, 1989 Knowledge Acquisition Journal Vol.1 No.2

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. AQUINAS interviews experts directly and helps them organize, analyze, test, and refine their knowledge bases. Expertise from multiple experts or other knowledge sources can be represented and used separately or combined, giving consensus and dissenting opinions among groups of experts. Results from user consultations are derived from information propagated through hierarchies. ETS and AQUINAS have assisted in building knowledge-based systems for several years at The Boeing Company. This paper describes recent progress on AQUINAS in the areas of knowledge base performance measurement, knowledge base maintenance, interacting trait constraints, consultation graphics, and eliciting strategic and procedural knowledge. Experiments show how AQUINAS can automatically improve knowledge bases and even suggest new problem-solving information. Forms of interactive and automatic machine learning employed by AQUINAS are also discussed.

ACQUIRING AND VERIFYING CONTROL KNOWLEDGE FOR A BLACKBOARD SYSTEM, Lawrence S. Baum, David B. Shema, John H. Boose, Jeffrey M. Bradshaw, Proceedings of the Fourth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff

Knowledge acquisition technology can be tied to methods for verification of knowledge-based systems. Preliminary experiments have shown that testing and review of knowledge while it is being elicited can help overcome portions of this problem. This paper focuses on a knowledge-acquisition-oriented testing method for verifying knowledge-based systems. The method uses test cases supplied by an expert that include the expected or ideal behavior of the system given a set of conditions. Test cases are matched against actual system results to measure system performance in terms of accuracy. In certain instances, test cases are also used to automatically improve the knowledge base. To extend this work to a new problem domain, we have linked Aquinas, a knowledge acquisition workbench, to Erasmus, a blackboard system, for an aircraft cockpit information management application. This is a prototype of a future real-time system. Aquinas interviews domain experts and generates knowledge sources for Erasmus that contain blackboard control heuristic information. This control information is used by the scheduler in Erasmus to select appropriate knowledge sources. Domain experts build test cases in Aquinas; these are automatically matched against Erasmus reports to measure control knowledge performance against the experts' expectations of performance. Aquinas also automatically tries to improve the experts' original knowledge bases by changing certain information and seeing if it improves knowledge base performance.

Aquinas, Axotl, and Folie à Deux: Doing Knowledge Acquisition for Decision Analysis Problems, Jeffrey M. Bradshaw, Stanley P. Covington, Peter J. Russo, and John H. Boose, 1989 Fourth AAAI-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff

This paper describes an automated approach to knowledge acquisition for decision analysis problems. This approach is being implemented in folie à deux , an "open architecture" task modeling environment that integrates components from Axotl, a knowledge-based decision analysis workbench, with those of Aquinas, a knowledge acquisition workbench based on personal construct theory. The decision analysis workbench provides modeling and inference capabilities for problems involving significant uncertainty, complex tradeoffs, or high stakes. The knowledge-based tools in Axotl can be configured with application-independent and application-specific knowledge to provide guidance and help in formulating, evaluating, and refining decision models represented in influence diagrams. This knowledge can be acquired and tested with the help of Aquinas and other knowledge acquisition tools that are part of Axotl. We describe the tools, techniques, and representations used for different kinds of knowledge, and discuss practical issues that have arisen in the application of folie à deux technology to R&D project selection decisions, process management, and long-term follow-up care for bone marrow transplant patients. The system derives its power in the integration of elements that we think will be the building blocks of future systems for automated support of complex decisions: a decision analysis workbench that relies on normative techniques; a knowledge-based system for provision of help in building formal decision models and to promote the re-use of domain knowledge; and a collection of knowledge acquisition tools tailored to the domain that allow such systems to be efficiently built and thoroughly tested.

From ETS to Aquinas: Six years of Knowledge Acquisition Tool Development, John H. Boose, Jeffrey M. Bradshaw, Catherine M. Kitto, David B. Shema, Proceedings of the Fourth AAAI-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff

Two major forces brought about the development of the knowledge acquisition tools ETS (the Expertise Transfer System) and Aquinas: technology push and application pull. Ideas from many areas were gradually integrated to meet the growing demands of knowledge-based systems problems at The Boeing Company. This paper briefly traces the history of this development and the sources of many of our ideas, describes features and the reasons that they were added, and illustrates typical applications at each stage of evolution. First a brief analysis of the forces that led to the development of our knowledge acquisition tools is presented. Then a history of tool growth is shown. Included are various features that were introduced and the types of help they provided. Typical applications at each stage of development are illustrated. Finally, we list the next hurdles to be overcome and show how we expect to overcome some of them in the next few years.

Capturing Design Knowledge for Engineering Trade Studies, John H. Boose, David B. Shema, Jeffrey M. Bradshaw, 1990 Proceedings of the Fifth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff

Currently, much of the information regarding decision alternatives and trade-offs made in the course of a major program development effort is not represented or retained in a way that permits computer-based reasoning over the life cycle of the program. The loss of this information results in problems in tracing design alternatives to requirements, in assessing the impact of change in requirements, and in configuration management. To address these problems, we are studying the problem of building an intelligent, active corporate memory facility which would provide for the capture of the requirements and standards of a program, analyze the design alternatives and trade-offs made over the program's lifetime, and examine relationships between requirements and design trade-offs. Early phases of the work have concentrated on design knowledge capture for the Space Station Freedom. We have demonstrated and are extending tools that help automate and document engineering trade studies (the topic of this paper), and we are developing another tool to help designers interactively explore design alternatives and constraints.

Sharable Ontologies as a Basis for Communication and Collaboration in Conceptual Modeling, Jeffrey M. Bradshaw, Peter D. Holm, John H. Boose, Douglas Skuce, Timothy C. Lethbridge, Proceedings of the Seventh Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff

We are interested in improving collaboration among researchers, domain experts, and knowledge engineers through better knowledge sharing. Building models is not only a way to formulate domain knowledge, but serves more importantly as a means to communicate and come to understand the evolving problem space. An important prerequisite to effective communication is mutual agreement on important terms and concepts, the ontology of the domain. We give an overview of the CODE4 conceptual modeling representation and the DDUCKS modeling environment. We focus on how different classes of ontologies are developed within the tools: top level ontologies with very general concepts, ontologies with modeling concepts, and ontologies for particular domains. We are evaluating theories and contexts as mechanisms for partitioning and managing relationships between groups of concepts. We describe some of the mechanisms we are exploring for exchanging these ontologies with other research groups, and conclude by reviewing important research issues for future work.

Mediating Representations for Knowledge Acquisition, Jeffrey M. Bradshaw & John H. Boose

Recent work in knowledge acquisition has emphasized that the creation of knowledge bases is a constructive modeling process, and not simply a matter of "expertise transfer" or "knowledge extraction". More fundamentally, modeling is purposive, that is, to be involved in modeling is necessarily to be engaged in using the model in some particular setting for particular reasons that together determine what should be modeled, how to model it, and what can be ignored. Together, the criteria of purpose and cost-effectiveness determine how additional pragmatic issues should be resolved such as who the users of the model are, how it ought to be presented in order to be both usable and useful, and how it will be maintained over its projected lifetime.

Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the-Art, John H. Boose, Brian R. Gaines

Notes from the organizers of a series of knowledge acquisition workshops are presented here. The state-of-the-art in knowledge acquisition research is briefly described. Then the technology of interactive knowledge acquisition is discussed, including a descriptive framework, dimensions of use, and research patterns. Finally, dissemination of information from knowledge acquisition workshops is detailed.

Folie à Deux: Integrating Aquinas, a Personal-Construct-Based Knowledge Acquisition Workbench, with Axotl, a Knowledge-Based Decision Analysis Workbench, Jeffrey M. Bradshaw, Stanley P. Covington, Peter J. Russo, and John H. Boose, 1989 Proceedings of the Fifth Workshop on Uncertainty and AI.

This paper describes the development of folie à deux , an "open architecture" task modeling environment that integrates components from Axotl, a knowledge-based decision analysis workbench, with those of Aquinas, a knowledge acquisition workbench based on personal construct theory.