Automated Knowledge Acquisition Tool Research Articles

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

There's no theory better than that of a practical tool

This special issue of The Knowledge Engineering Review includes three papers on the subject of automated knowledge-acquisition tools. Gaines and Shaw (1993) review knowledge acquisition tools based on personal-construct psychology; Birmingham and Klinker (1993) review tools based on predefined models of problem-solving methods; and Eriksson and Musen (1993) review metalevel tools that developers may use to build other knowledge acquisition tools. Although this trio of papers by no means summarizes all recent developments in computer-based assistance for knowledge engineering, these contributions provide examples of three important research frontiers that, in many ways, trace their origins to early expert systems research.

Integrated knowledge base building environments

Knowledge base building environments must progress in two important directions: (i) increased participation of domain experts in the knowledge design process through new computational models and effective man-machine interfaces; and (ii) automated knowledge acquisition tools to facilitate the overt expression of knowledge. This paper presents the integration of a knowledge acquisition methodology with a performance system. The resulting architecture represents a combination of techniques from psychology, cognitive sciences and artificial intelligence. New dimensions emerge from this implementation and integration both at the theoretical and practical levels. The overall system is not linked to a particular control structure and is not task dependent. We discuss the value of intermediate representations in this context and the role of different approaches to the induction process. Topological induction is particularly efficient in the elicitation process and stresses the importance of interactive inductive techniques with participation from the experts. While the knowledge acquisition tool provides an analysis and structuring of the domain knowledge, the control is implemented using the performance system's interface. Therefore, both modules participate in the overall knowledge acquisition process. Beyond the integration of these knowledge acquisition and performance systems, the architecture can also be integrated with databases, text analysis techniques, and hypermedia systems.

Automated knowledge acquisition for a computer hardware systhesis system

The MICON Synthesizer Version 1 (M1) is a rule-based system which produces a complete small computer design from a set of abstract specifications. M1's design ability depends on the encoding of large amounts of domain knowledge. An automated knowledge acquisition tool, CGEN, works synergistically with M1 by gathering knowledge of how to build and when to use various computer structures. This paper overviews the operation of CGEN by providing an example of the types of knowledge acquired and the mechanisms employed. A novel knowledge-intensive generalization scheme is presented, as well as an extensive series of experiments demonstrating CGEN's capabilities. The results of the experiments show CGEN is able to build working knowledge-bases without the intercession of a knowledge engineer.

Knowledge Acquisition: Human Factors Issues

Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected manually via a series of interviews held between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Several human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. This paper provides a discussion of current manual/automated acquisition techniques, human factors issues associated with knowledge acquisition, and the ways in which several acquisition methodologies have confronted human factors issues.

Formulation of Expert System Knowledge

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

Knowledge acquisition at the metalevel: creation of custom-tailored knowledge-acquisition tools

Automated knowledge-acquisition tools can assist system builders in the creation of knowledge bases for expert systems. An important class of such tools includes programs that contain detailed models of families of application tasks. Users enter knowledge about particular applications into these tools in terms of the predefined task models that the tools incorporate. Construction of such tools, however, has itself proven to be laborious. PROTÉGÉ is an interactive program that assists knowledge engineers in the construction of task models, and that automatically generates custom-tailored, graphical knowledge-acquisition tools based on those models. Domain experts independently use the knowledge-acquisition tools that PROTÉGÉ generates to enter the knowledge that defines individual applications. The methodology simplifies the development of task-based knowledge-acquisition aids, and allows system builders to separate the difficult, model-building part of knowledge acquisition from the more straightforward entry of content knowledge.

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.

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.