The role of knowledge management in hierarchical model development
Authors: 
Y.-M. Huang, J. W. Rozenblit, J. HuAuthors Info & Claims
Pages 584 - 592
Abstract
The methods for transforming real-world problem into simulation models are being increasingly explored with the availability of inexpensive computing power. In general, traditional model building procedures involve a lengthy problem formulation and interaction between the analyst and the client. Furthermore, after the model definition is arrived at, the simulation model is often programmed manually. Recent developments in simulation modeling have focused on automatic model generation employing artificial intelligence (AI) techniques. Such developments focus on the transfer of the user's knowledge about the system into an executable simulation model. Current techniques still lack effective knowledge acquisition tools and a global database from which model alternatives can be generated. In this paper, a set of knowledge bases (KBs) will be proposed to aid in the hierarchical model construction.
References
[1]
Balci, O. and Nance, R. E. (1987). Simulation model development environments: a research prototype. J. Opl Res. Soc. Vol. 38, No. 8, 753--763.]]
[2]
Balmer, D. W. and Paul, R. J. (1986). CASM-the right environment for simulation. J. Opl Res. Soc. 37, 443--452.]]
[3]
Doukidis, G. and Paul, R. J. (1985). Research into expert systems to aid simulation model formulation. J. Opl Res. Soc. Vol. 36, No. 4, 319--325.]]
[4]
Erman, D. L., Hayes-Roth, F., Lesser, V. R., and Reddy, D. Raj. (1980). The HEARSAY-II speech understanding system: Integrating knowledge to resolve uncertainty. ACM Computing Survey 12:213--253.]]
[5]
Ericsson, K. A. and Simon, H. A. (1984). Protocol Analysis: Verbal Report as Data. Cambridge, Mass: MIT Press.]]
[6]
Gaines, B. R. (1987). An overview of knowledge-acquisition and transfer. Int. J. Man-Machine Studies, No. 26, 453--472.]]
[7]
Hu, J., Huang, Y. M., and Rozenblit, J. W. (1989). FRASES - A knowledge representation scheme for engineering design. Advances in AI and Simulation, SCS Simulation Series, vol. 20, no. 4, 141--146.]]
[8]
Hu, Jhyfang and Rozenblit, J. W. (1989). Knowledge acquisition based on representation (KAR) for design model development. to appear in: Knowledge-Based Simulation: Methodology and Applications, (ed. P. Fish-wick and R. Modejeski), Springer-Verlag, NY.]]
[9]
Hu, Jhyfang (1989). Towards a knowlege-based design support environment for design automation and performance evaluation. Ph.D. Dissertation, University of Arizona, Tucson, Arizona.]]
[10]
Huang, Y. M. (1987). Building an expert system shell for design model synthesis in logic programming. Master Thesis, Dept. of Electrical and Computer Engineering, Univ. of Arizona, Tucson, Arizona.]]
[11]
Kahn, G. (1985). Strategies for knowledge acquisition. IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 7, 511--522.]]
[12]
Kessel, K. L. (1986). Methodological tools for knowledge acquisition. Proceedings of the 1986 IEEE International Conference on System, Man, and Cybernetics, Atlanta, GA.]]
[13]
Murray K. J. and Sheppard, S. V. (1988). Knowledge-based simulation model specification, Simulation, March, 1988, 112--119.]]
[14]
Nii, H. P. (1986). Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architecture. The AI Magazine, Summer, 1986, 38--53.]]
[15]
Olson, J. R. and Rueter, H. H. (1987). Extracting expertise from experts: methods for knowledge acquisition. Expert Systems, August 1987, Vol. 4, No. 3, 152--168.]]
[16]
Pan, N. (1989). A lisp-based shell for model structuring in system design. Master Thesis, Dept. of ECE, Univ. of Arizona, Tucson, Arizona.]]
[17]
Reddy Y. V. et al., (1986). The knowledge-based simulation system. IEEE Software, March, 1988, 26--37.]]
[18]
Ritchie, I. C. (1984). Knowledge acquisition by computer induction. Proceedings of UNICOM Seminar, London, England.]]
[19]
Rozenblit, J. W. and Hu, J. (1989). Experimental frame generation in a knowledge-based system design and simulation environment. To appear in Modeling and Simulation Methodology: Knowledge System Paradigms, (M. Elzas et. al., eds), North Holland, Amsterdam.]]
[20]
Rozenblit, J. W. and Huang, Y. M. (1987). Constraint driven generation of model structures. Proceedings of the 1987 Winter Simulation Conference, Atlanta, Georgia.]]
[21]
Rozenblit, J. W. and Y. M. Huang (1989). Rule-based generation of model structures in multifacetted modeling and system design, ORSA Journal on Computing (in review).]]
[22]
Waterman, D. A. and Newell, N. (1971). Protocol analysis as a task for artificial intelligence, Artificial Intelligence, 2, p. 285.]]
[23]
Waterman, D. A. (1986). A Guide to Expert Systems, Addison-Wesley, Reading, Mass.]]
[24]
Zeigler, B. P. (1984). Multifacetted Modeling and Discrete Event Simulation, Academic Press, London.]]
Index Terms
- The role of knowledge management in hierarchical model development
Recommendations
Discovering role-based virtual knowledge flows for organizational knowledge support
In knowledge-intensive work environments, workers need task-relevant knowledge and documents to support the execution of tasks. A knowledge flow (KF) represents an individual's or group's knowledge-needs and referencing behavior of codified knowledge ...
The principle of knowledge management
The central dogma of Molecular Biology could be a reference for the principle of knowledge management. The knowledge-creating bases learned from DNA could be organised to describe the knowledge creation process. The knowledge assets play roles in ...
Enterprise knowledge management model: a knowledge tower
One of the most important aspects of knowledge management (KM) is to create a system that is capable of providing mechanisms and methodologies allowing the right knowledge to be at the right place and at the right person as well as at the right time ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 1989
1139 pages
Copyright © 1989 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- IIE: Institute of Industrial Engineers
- TIMS/CS
- SES: SES
- ORSA: Operations Research Society of America
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- IEEE-CS: Computer Society
- NIST: National Institute of Standards & Technology
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 October 1989
Check for updates
Qualifiers
- Article
Acceptance Rates
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 177Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)1
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in