Modelling Expertise for KBS Development

The benefits of a qualitative modelling approach to building knowledge-based systems include better project control, communication between participants, more accurate validation and easier maintenance. Knowledge acquisition from experts will be more successful if a conceptual model is developed from a knowledge level description, to act as a publicly examinable basis for system design. Analytical tools are available to structure both content and process knowledge on paper, before any commitment is made to implementation. The role of abstraction in developing models of generic tasks as a set of templates for conceptual model development is reviewed, with particular regard to knowledge acquisition tools. Modelling problem-solving methods, especially for constructive tasks such as planning, is more difficult than modelling content knowledge. Epistemological support for conceptual abstraction, particularly at middle levels, remains inadequate.

[1]  Mark A. Musen,et al.  Conceptual models of interactive knowledge acquisition tools , 1989 .

[2]  Jay Liebowitz Beyond decision support systems: the role of operations research in expert systems , 1988 .

[3]  Johanna D. Moore,et al.  Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development , 1984, IEEE Transactions on Software Engineering.

[4]  Colin Eden,et al.  Messing about in problems , 1983 .

[5]  Paul N. Finlay,et al.  Experiences in Developing an Expert System for MBA Admissions , 1989 .

[6]  John C. Kunz,et al.  From Classic Expert Systems to Models: Introduction to a Methodology for Building Model-Based Systems , 1989 .

[7]  Robert M. O'Keefe,et al.  Expert Systems and Operational Research-Mutual Benefits , 1985 .

[8]  John V. Carlis,et al.  Conceptual data modeling of expert systems , 1989, IEEE Expert.

[9]  Björn Wahlström On the use of models in human decision-making , 1988 .

[10]  Donald A. Norman,et al.  Some observations on mental models , 1987 .

[11]  Viviane Jonckers,et al.  A Framework for Modeling Programming Knowledge , 1990, AI Commun..

[12]  John G. Gammack,et al.  Constructive interaction in knowledge engineering , 1990 .

[13]  Sandra Marcus,et al.  Automating Knowledge Acquisition for Expert Systems , 1988 .

[14]  Alessandro Saffiotti,et al.  An AI view of the treatment of uncertainty , 1987, The Knowledge Engineering Review.

[15]  Fernando Orejas,et al.  Role of Abstraction in Program Development - Response , 1986, IFIP Congress.

[16]  W. Bruce Croft,et al.  Relating Human Knowledge of Tasks to the Requirements of Plan Libraries , 1989, Int. J. Man Mach. Stud..

[17]  William J. Clancey,et al.  Heuristic Classification , 1986, Artif. Intell..

[18]  Larry J. Eshelman,et al.  MOLE: A Tenacious Knowledge-Acquisition Tool , 1987, Int. J. Man Mach. Stud..

[19]  Brian R. Gaines,et al.  Methodology in the Large: Modeling All There Is , 1984 .

[20]  B. Chandrasekaran,et al.  Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design , 1986, IEEE Expert.

[21]  Robert Worden Processes of knowledge and software , 1989, Knowl. Based Syst..

[22]  Lawrence M. Fagan,et al.  Use of a Domain Model to Drive an Interactive Knowledge-Editing Tool , 1987, Int. J. Man Mach. Stud..

[23]  D. Broadbent,et al.  On the Relationship between Task Performance and Associated Verbalizable Knowledge , 1984 .

[24]  Derek Partridge,et al.  Input-Expectation Discrepancy Reduction: A Ubiquitous Mechanism , 1985, IJCAI.

[25]  Gary S. Kahn,et al.  The Mud System , 1986, IEEE Expert.

[26]  Elizabeth S. Cordingley,et al.  Knowledge elicitation techniques for knowledge-based systems , 1989 .

[27]  Mark A. Musen Automated generation of model-based knowledge acquisition tools , 1989 .

[28]  D. L. Medin,et al.  Concepts: static definitions or context -dependent representations? , 1986 .

[29]  Stefan Wrobel,et al.  Design Goals for Sloppy Modeling Systems , 1988, Int. J. Man Mach. Stud..

[30]  Enrico Motta,et al.  Knowledge acquisition as a process of model refinement , 1990 .

[31]  J. A. Bubenko Information analysis and conceptual modeling , 1988 .

[32]  Neville Moray Intelligent Aids, Mental Models, and the Theory of Machines , 1987, Int. J. Man Mach. Stud..

[33]  Bob Wielinga,et al.  Models of Expertise in Knowledge Acquisition , 1989 .

[34]  Alison Connolly Knowledge elicitation: Principles, techniques and applications , 1991 .

[35]  William A. Gale Knowledge-Based Knowledge Acquisition for a Statistical Consulting System , 1987, Int. J. Man Mach. Stud..

[36]  Bob Wielinga,et al.  Use of Models in the Interpretation of Verbal Data , 1987 .

[37]  William J. Clancey Viewing knowledge bases as qualitative models , 1989, IEEE Expert.

[38]  Elliot Soloway,et al.  Assessing the Maintainability of XCON-in-RIME: Coping with the Problems of a VERY Large Rule-Base , 1987, AAAI.

[39]  G. M. Nijssen,et al.  The Entity-Relationship Data Model Considered Harmful , 1990 .

[40]  Ian M. Neale,et al.  First generation expert systems: a review of knowledge acquisition methodologies , 1988, The Knowledge Engineering Review.

[41]  Katharina Morik Acquiring Domain Models , 1987, Int. J. Man Mach. Stud..

[42]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[43]  Ray Waddington,et al.  Task-Related Knowledge Structures: Analysis, Modelling and Application , 1988, BCS HCI.

[44]  Emilie M. Roth,et al.  Cognitive Task Analysis: An Approach to Knowledge Acquisition for Intelligent System Design , 1989 .

[45]  L. Suchman Plans and situated actions , 1987 .

[46]  Stephen Regoczei,et al.  Creating the Domain of Discourse: Ontology and Inventory , 1987, Int. J. Man Mach. Stud..

[47]  B. Chandrasekaran,et al.  Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples , 1988, The Knowledge Engineering Review.