Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-19T02:07:39.469Z Has data issue: false hasContentIssue false
  • English
  • Français

Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

Published online by Cambridge University Press:  07 July 2009

B. Chandrasekaran
B. Chandrasekaran
Affiliation:
Laboratory for Artificial Intelligence Research, Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210
Get access Rights & Permissions [Opens in a new window]

Abstract

The level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 3 , Issue 3 , September 1988 , pp. 183 - 210
Copyright
Copyright © Cambridge University Press 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allemang, Dean, Tanner, Michael C, Bylander, Tom and Josephson, John R, 1987. “On the computational complexity of hypothesis assembly” Proceedings of IJCAI-87, Milan, Italy, August, 1987.Google Scholar
Bennett, James, 1986. “COAST: A task-specific tool for reasoning about configurations” In: Proc A A A I Workshop on High-Level tools, AAAI, Shawnee Park, Ohio, 1986.Google Scholar
Brown, D C, 1984. Expert Systems for Design Problem-Solving using Design Refinement with Plan Selection and Redesign, PhD thesis, The Ohio State University, August.Google Scholar
Brown, David C and Chandrasekaran, B, 1986. “Knowledge and control for a mechanical design expert system” IEEE Computer 19, pp. 92–101, July.CrossRefGoogle Scholar
Buchanan, B, Sutherland, G and Feigenbaum, E A, 1969. “Heuristic DENDRAL: A program for generating explanatory hypotheses” Organic Chemistry.Google Scholar
Bylander, T and Mittal, S, 1986. “CSRL: A language for classificatory problem solving and uncertainty handling” AI Magazine 7(3), pp. 66–77.Google Scholar
Bylander, T, Smith, J and Svirbely, J, 1986. “Qualitative representation of behavior in the medical domain” In: Proceedings of The Fifth Conference on Medical Informatics, pp. 7–11. Conference on Medical Informatics,Washington, DC,October 26–30, 1986.Google Scholar
Bylander, Tom and Johnson, Todd R, 1987. Structured Matching OSU CIS LAIR Technical Report.Google Scholar
Chandrasekaran, B, 1983. “Towards a taxonomy of problem solving types” AI Magazine pp. 9–17, Winter/Spring.Google Scholar
Chandrasekaran, B, 1986. “Generic tasks in knowledge-based reasoning: high level building blocks for expert system design” IEEE Expert 1(3), pp. 23–30.CrossRefGoogle Scholar
Chandrasekaran, B and Tanner, M, 1986. “Uncertainty handling in expert systems: Uniform vs. task-specific formalisms” Uncertainty in Artificial Intelligence, Kanal, L N and Lemmer, J (Eds). North Holland Publishing Company, pp. 35–46.CrossRefGoogle Scholar
Chandrasekaran, , 1987. “Towards a functional architecture for intelligence based on generic information processing tasks” In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 1183–1192, Milan,Italy,August, 1987.Google Scholar
Chandrasekaran, B, Smith, J and Sticklen, J, 1987. Deep Models and Their Relation to Diagnosis Technical report. Invited paper, Toyoba Foundation Symposium on Artificial Intelligence in Medicine, Tokyo, Japan, August 1986, available as technical report from Laboratory of AI Research, Ohio State University.Google Scholar
Chandrasekaran, B, 1987. “What kind of information processing is intelligence? A perspective on AI paradigms and a proposal”. This paper will appear in Source Book on the Foundations of AI Partridge, and Wilks, (Eds.), Cambridge University Press.Google Scholar
Chandrasekaran, B, Tanner, M C and Josephson, J R, 1988. “Explanation: the role of control strategies and deep models” In: Expert Systems: The User Interface, James, Hendler (Ed.), Ablex Publishing Corporation, Norwood, New Jersey 07648, pp. 219–247.Google Scholar
Chandrasekaran, B, 1988. Design: An Information Processing Level Analysis technical report. The Ohio State University, Laboratory for AI Research, Columbus, Ohio 43210.Google Scholar
Clancey, W J, 1981. “NEOMYCIN: Reconfiguring a rule-based expert system for application to teaching” In: Proc. Seventh International Joint Conference on Artificial Intelligence, pp. 829–836. IJCAI,Vancouver.Google Scholar
Clancey, W J, 1985. “Heuristic classification” Artificial Intelligence 27(3), pp. 289–350.CrossRefGoogle Scholar
Duda, R O, Gaschnig, J G and Hart, P E, 1980. “Model design in the prospector consultant system for mineral exploration” In: Expert System in the Microelectronic Age, Michie, D (Ed.), pp. 153–167, Edinburgh University Press.Google Scholar
Friedland, P, 1979. Knowledge-based Experiment Design in Molecular Genetics. PhD thesis, Stanford University, Computer Science Department.Google Scholar
Goel, A, Soundararajan, N and Chandrasekaran, B, 1987. “Complexity in Classificatory Reasoning” In: Proc. National Conference on Artificial Intelligence, pp. 421–425. Seattle,Washington,July 13–18, 1987.Google Scholar
Gomez, F and Chandrasekaran, B, 1981. “Knowledge organization and distribution for medical diagnosis” IEEE Transactions on Systems, Man, and Cybernetics, SMC–11(1), pp. 34–42, January.Google Scholar
Hashemi, S, Hajek, B K, Miller, D W, Chandrasekaran, B and Josephson, J R, 1986. “Expert systems application to plant diagnosis and sensor data validation” In: Proceedings of the Sixth Power Plant Dynamics Control and Testing Symposium, Knoxville, Tennessee, April, 1986.Google Scholar
Johnson, T, 1986. “HYPER: The hypothesis matcher tool” In: Proceedings of Expert Systems Workshop, pp. 122–126. Defense Advanced Research Projects Agency, Pacific Grove, CA, April 16–18.Google Scholar
Johnson, K, Sticklen, J and Smith, J W, 1988. “IDABLE—application of an intelligent data base to medical systems” In: Proceedings of the AAAI Spring Artificial Intelligence in Medicine Symposium, pp. 43–44. American Association for Artificial Intelligence, Stanford University, March 22–24.Google Scholar
Josephson, J R, Smelters, D, Welch, A K, Fox, R, Flores, G and Lyndes, D, 1988. Generic Task Toolset DRACO Release—Beta Test Including RA Technical report. The Ohio State University, Computer & Information Science Department, Laboratory for Artificial Intelligence Research, February.Google Scholar
Josephson, J R, Chandrasekaran, B, Smith, J W and Tanner, M C, 1987. “A mechanism for forming composite explanatory hypotheses” IEEE Trans, on Systems, Man and Cybernetics pp. 445–454.Google Scholar
Marcus, Sandra and McDermott, John, 1987. SALT: A Knowledge Acquisition Tool for Propose-and-Revise Systems Technical report. Department of Computer Science, Carnegie-Melton University, Pittsburgh, PA.Google Scholar
McDermott, J, 1982. “RI: A rule-based configurer of computer systems” Artificial Intelligence 19(1), pp. 39–88.CrossRefGoogle Scholar
Miller, R A, Pople, H E and Myers, J D, 1984. “Internist-1, an experimental computer-based diagnostic consultant for general internal medicine” Readings in Medical Artificial Intelligence, Addison-Wesley Publishing, pp. 190–209.Google Scholar
Mittal, S, 1980. Design of A Distributed Medical Diagnosis and Data Base System PhD thesis. The Ohio State University.Google Scholar
Myers, D R, Davis, J F and Herman, D, 1988. “A task oriented approach to knowledge-based systems for process engineering design” Computers and Chemical Engineering, Special Issue on AI in Chemical Engineering Research and Development, August.CrossRefGoogle Scholar
Laird, J E, Newell, A and Rosenbloom, P S, 1987. “SOAR: An architecture for general intelligence” Artificial Intelligence 33, pp. 1–64.CrossRefGoogle Scholar
Punch, W F, Tanner, M C and Josephson, J J, 1986. “Design Considerations for PEIRCE, a high-level language for hypothesis assembly” Expert Systems in Government Symposium pp. 279–281, October.Google Scholar
Shortlifie, E H, 1976. Computer-based Medical Consultations: MYCIN Elsevier/North-Holland Inc.Google Scholar
Shum, S K, Davis, J F, Punch, W F III and Chandrasekaran, B, 1988. “An expert system approach for malfunction diagnosis in chemical plants” Computers and Chemical Engineering 12(1), pp. 27–36.CrossRefGoogle Scholar
Smith, J W, Svirbely, J R, Evans, C A, Straum, P, Josephson, J R and Tanner, M C, 1985. “RED: A red-cell antibody identification expert module” Journal of Medical Systems 9(3), pp. 121–138.CrossRefGoogle Scholar
Sticklen, J, Chandrasekaran, B and Josephson, J, 1985. “Control issues in classificatory diagnosis” In: Proceedings of The 9th International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence,University of California,Los Angeles, CA,August, pp. 18–24.Google Scholar
Sticklen, Jon, 1987. “MDX2: An integrated medical diagnostic system” PhD. Dissertation, Department of Computer and Information Science, The Ohio State University, Columbus, Ohio 43210.Google Scholar
Tanner, M and Bylander, T, 1985. “Application of the CSRL language to the design of expert diagnosis systems: The auto-mech experience” Artificial Intelligence in Maintenance. Noyes Publications, Park Ridge, N.J.Google Scholar