Knowledge Acquisition Tool Research Articles

The acquisition, verification, and explanation of design knowledge

Researchers from Lockheed and Stanford University are currently engaged in a joint project investigating the acquisition of design knowledge to aid in the modification and reuse of designs. In our approach acquisition, verification, and explanation are integral components of the design capture process. We are using the HELIOS digital design system (developed at Stanford) as a framework for experimenting with knowledge acquisition and explanation tools. These tools will be used to capture design rationale directly from the expert as the design evolves. Verification capabilities are also being developed to validate design specifications and explore the consequences of various design alternatives.

A cognitively valid knowledge acquisition tool

As more (and more) knowledge-based systems are designed for operational use, it becomes apparent that the knowledge engineering (KE) process of transferring domain expertise into the system consumes the single largest block of development time (Feigenbaum, 1977; Hayes-Roth, Waterman, & Lenat, 1983). To reduce the time required for this stage, numerous tools have been developed that automate or systematize the elicitation and transfer of knowledge from expert to knowledge base. Among these advances are developments in machine learning that enable an expert system to acquire its knowledge from "experience" (Michalski, 1980; Michie, 1982), expert system shells that allow domain experts to write their own rules (e.g., MacSMARTS), and knowledge engineering tools that assist a knowledge engineer in his/her work. The material reported here describes BDM's development of a computer-based set of tools, known collectively as BDM-KAT.

A survey of knowledge acquisition techniques and tools

Knowledge acquisition tools can be associated with knowledge-based application problems and problem-solving methods. This descriptive approach provides a framework for analysing 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.

Conceptual models of interactive knowledge acquisition tools

Workers in artificial intelligence (AI) have developed many interactive programs that assist in the knowledge-acquisition process. Because of the diverse nature of these tools, it is often difficult to understand how each one relates to all the others. This paper describes a taxonomy for knowledge-acquisition aids that is based on the terms and relationships that a given tool uses to establish the semantics of a user's entries. Such semantic assumptions, or conceptual models, have important implications for how a knowledge-acquisition tool is used and to what degree it can assist its users in analysing new applications at the knowledge level. Furthermore, when the conceptual model of a knowledge-acquisition tool can be made explicit, knowledge engineers can use metalevel programs to edit that conceptual model, creating knowledge-acquisition aids that are custom-tailored for particular applications. One such metalevel tool, PROTEGE, has been developed to allow editing of the conceptual models of programs that acquire knowledge for tasks that can be solved via the method of skeletal-plan refinement.

Automatic knowledge acquisition and maintenance for semantic query optimization

The authors present an approach to acquiring knowledge from previously processed queries. By using newly acquired knowledge together with given semantic knowledge, it is possible to make the query processor and/or optimizer more intelligent so that future queries can b processed more efficiently. The acquired knowledge is in the form of constraints. While some constraints are to be enforced for all database states, others are known to be valid for the current state of the database. The former constraints are statistic integrity constraints, while the latter are called dynamic integrity constraints. Some situations in which certain dynamic semantic constraints can be automatically extracted are identified. This automatic tool for knowledge acquisition can also be used as an interactive tool for identifying potential static integrity constraints. The concept of minimal knowledge base is introduced, and a method to maintain the knowledge base is presented. An algorithm to compute the restriction (selection) closure, i.e. all deductible restrictions, from a given set of restrictions, join predicates (as given in a query), and constraints is given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Concept classifier for knowledge acquisition in frame-based systems

Frame-based systems that employ inheritance networks as a form of knowledge representation have a number of inherent knowledge acquisition problems, one of the most significant being the transfer to the representation system of knowledge itself. The problem of concept classification, and specifically that of determining the location of a new concept in an existing network inheritance hierarchy, is discussed here using an experimental knowledge-base editor, KRE. Tools which support the process of knowledge base construction must allow the user to concentrate on the domain problems, and not on low level, representation system decisions. KRE, written in C, is a knowledge acquisition tool which assists the knowledge engineer by using an interactive acquisition strategy during the process of concept classification. The processes of classification, and its advantages over other knowledge representation systems, are presented.

ASTEK: A multi-paradigm knowledge acquisition tool for complex structured knowledge

Knowledge-based systems can require large, highly complex and varied forms of knowledge. An effective knowledge acquisition tool to support such a system should allow the user to transfer and manipulate the different forms knowledge in a manner that is clear and intuitive. ASTEK is a knowledge acquisition tool that provides multiple paradigms for knowledge editing while maintaining a single, consistent framework designed using natural language discourse concepts.

Uses of repertory grid-centred knowledge acquisition tools for knowledge-based systems

Repertory grid-centred 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-centred tools such as the Expertise Transfer System (ETS), AQUINAS, KITTEN, and KSSO. 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.

First generation expert systems: a review of knowledge acquisition methodologies

AbstractThis paper reviews a wide range of knowledge acquisition techniques in the context of attempts to achieve a systematic methodology. These have been poorly documented by expert system builders, who are often inclined to overvalue textbooks and the ways experts themselves claim they solve problems. No one method has a universal advantage; each has some value. Techniques should be selected to suit the domain, the task, the expert and the knowledge engineer. Knowledge acquisition involves creating a conceptual model of expert knowledge and reasoning, from analysis of data elicited by these techniques. A survey of the literature indicates increasing emphasis on tools for knowledge acquisition, used directly by experts. Several projects currently directed towards providing a proper epistemological foundation for knowledge acquisition are discussed and compared. None has yet produced a complete epistemologically sound methodology; however, recognition of the need to create a conceptual model at the knowledge level (rather than the symbol level) is an important advance. An extensive bibliography is appended.

VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking

VT (vertical transportation) is an expert system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company. Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible. In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it. VT uses domain-specific knowledge to guide its backtracking search for successful refinements. The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool. SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution.

MOLE: a knowledge acquisition tool that buries certainty factors

MOLE is a knowledge acquisition tool for helping experts build systems that do differential diagnosis. Diagnostic expert systems often have to rely upon inferences that involve some degree of uncertainty. Typically, the tentativeness of the rules of inference is represented by certainty factors or some other cardinal measure of support. Unfortunately, this information is difficult to acquire from the experts. This paper describes how MOLE is able to dispense with certainty factors. By integrating into its problem-solving method several heuristic assumptions about how evidence relates to hypotheses, and by including in its knowledge acquisition process a way of generalizing the experťs preferences, MOLE does not need to elicit certainty factors from the domain experts or to internally represent the degree of support of an inference rule with certainty factors. This facilitates knowledge acquisition with no loss to diagnostic performance.

Refining problem-solving knowledge in repertory grids using a consultation mechanism

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.

Knowledge Acquisition Systems For Rapid Prototyping Of Expert Systems

AbstractThis paper describes trends in knowledge support environments – integrated interactive knowledge acquisition systems and expert system shells – that can provide a specialist community with tools supporting a wide range of knowledge processes. These systems extrapolate the trend from human knowledge engineering, through automated interviewing of the expert, to continuing on-line access to both knowledge acquisition and application processes. In knowledge support systems the distinctions between expert, knowledge engineer and client roles are deliberately blurred, and a diversity of knowledge processes and changing roles are supported within an entire interacting community. A prototype knowledge support system is described with examples of some ofthe knowledge acquisition and application tools provided. It is suggested that such systems provide a major knowledge-based technology with commercial implications and applications going beyond those currently envisioned for expert systems.

SMR (simulating medical reasoning): an expert shell for non-AI experts

SMR is an expert system shell designed to put the tools for knowledge acquisition directly into the hands of the domain expert. Since the knowledge base is represented as free text within a simplified syntactic structure, it is intelligible to anyone familiar with medical terminology. The knowledge base includes the rules for inference making as well as data groupings and protocols to facilitate case recording. In this paper, SMR is presented from the expert's point of view, describing the rule syntax and procedures for formulating the required diagnostic or therapeutic knowledge in the chosen domain. Similarly, the end-user's application of the system to patient data and the provisions for exploring and explaining the system's conclusions and reasoning processes are detailed. Avoiding tedious and often inane dialog, the user enters all that is known about the patient and receives a report of the system's conclusions and recommendations followed by a list observations to be made in patient follow-up. An expert system for evaluating the diabetic patient is used to illustrate system operations.

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.

Taking backtracking with a grain of SALT

SALT is a knowledge-acquisition tool for generating expert systems that can use a propose-and-revise problem-solving strategy. The SALT-assumed method incrementally constructs an initial design by proposing values for design parameters, identifying constraints on design parameters as the design develops and revising design decisions in response to detection of constraint violations in the proposal. This problem-solving strategy provides the basis for SALT’s knowledge representation. SALT guides its interrogation of the domain expert using its identification of weaknesses in the knowledge base.

Knowledge acquisition and verification tools for medical expert systems.

Expert systems require large amounts of domain knowledge for non-trivial problem solving. Experience in developing such systems has shown that the processes of acquiring domain knowledge (knowledge acquisition) and of determining whether the knowledge is consistent, complete, and correct (knowledge verification) are major problems. This paper discusses various tools developed to assist in these two processes. These tools bring additional knowledge to bear, or provide better interfaces between a knowledge engineer and the expert system's knowledge.

HYDRA: a knowledge acquisition tool for expert systems that critique medical workup.

HYDRA is a computer-based knowledge acquisition tool under development to assist in the creation of expert systems which critique medical workup. To use HYDRA, a domain expert first outlines the recommended approaches to the workup of a chosen medical problem, using the Augmented Transition Network formalism. From this model, HYDRA produces a list of the various conditions for which critiquing comments may be required to react to all possible approaches that might be proposed by the user of the critiquing system. Domain-specific constraints can be used to restrict the number of conditions suggested. In this way, HYDRA assists the domain expert by providing a model for structuring the problem, and by breaking down the domain expert's work into a set of small, easily understood tasks.

Formal thought and narrative thought in knowledge acquisition

There are two different kinds of thought of interest to knowledge engineers. One is formal thought , as exemplified by logic and mathematics; the evaluation criterion for formal thought is truth . The second is narrative thought , as exemplified by metaphors, analogies, and gestalte ; the evaluation criterion for narrative thought is verisimilitude . To build a knowledge system which operates in the realm of narrative thought, one must build a model of expert knowledge of the domain, and such a model must contain not only the facts and heuristics used by the expert, but also the patterns of reasoning and most importantly the kinds of reasoning used by the expert. Three representative knowledge acquisition tools—RuleMaster, MOLE, and Aquinas—are briefly reviewed. It is suggested that the tools discussed, and the members of the classes each represents, fail to effectively support identifying and acquiring the patterns of reasoning and especially the kinds of reasoning used by experts in narrative domains.

Design for acquisition: principles of knowledge-system design to facilitate knowledge acquisition

The problem of knowledge acquisition is viewed in terms of the incongruity between the representational formalisms provided by an implementation (e.g. production rules) and the formulation of problem-solving knowledge by experts. The thesis of this paper is that knowledge systems can be designed to facilitate knowledge acquisition by reducing representation mismatch. Principles of design for acquisition are presented and applied in the design of an architecture for a medical expert system called MUM. It is shown how the design of MUM makes it possible to acquire two kinds of knowledge that are traditionally difficult to acquire from experts: knowledge about evidential combination and knowledge about control. Practical implications for building knowledge-acquisition tools are discussed.