Intelligent Assistance to Requirement Analysis

John Yen

Center for Fuzzy Logic, Robotics, and Intelligent Systems Research
Department of Computer Science
Texas A&M University
College Station, TX 77843-3112
Phone: (409) 845-5466
Fax : (409) 847-8578
Email: yen@cs.tamu.edu
Web page: http://www.cs.tamu.edu/faculty/yen/IDMreport.html

Keywords

Artificial intelligence, software engineering, fuzzy logic, requirement analysis, model identification

Project Award Information

Award Number: IRI-9257293
Duration: 5 year, 5th year
Title: Using Fuzzy Logic to Deal with Qualitative Requirements and Uncertainty in the Environment

Project Summary

Intelligent systems often need to deal with two kinds of uncertainty: (1) system requirements that are qualitative in nature, and (2) uncertainty about the state of the external environment. The primary objective of this research is to develop sound and practical techniques for dealing with these issues. To address the first issue, fuzzy logic based methodologies for specifying and validating qualitative requirements are being developed. Explicitly capturing the elasticity of the system's requirements facilitates the exploration of various trade-offs during the design stage and enables a more realistic validation of the implemented system. To address the second issue, systematic modeling techniques for designing fuzzy logic-based intelligent systems are being developed. Methodologies and techniques developed by this research will not only enhance the quality and the adaptability of the next generation of intelligent systems, but will also reduce the cost for designing and maintaining them.

Goals, Objectives, and Targeted Activities

The current focus of this project is to investigate the requirements of agent-based information systems. We are currently identifying various relationships between requirements of software agents. These relationships can lay the foundation for automated detection of conflicting agent requirements.

Indication of Success

An application of the inference scheme to House of Quality has been demonstrated, which were reported in ICSE96. We also developed a systematic framework for analyzing the trade-off of conflicting requirements using marginal rate of substitution in decision sciences. This research result was presented in RE97. This research has also resulted in a U.S. patent ``System and Method for Specifying Expert Systems''.

Project Impact

Human Resources: Five Ph.D. students participating the project have graduated. Jonathan Lee is currently an Associate Professor in Department of Computer Science and Information Engineering at National Central University in Taiwan. Frank Liu is currently an Assistant Professor in Computer Science Department at University of Missouri, Rolla. The other three PhD graduates are Swee Hor Teh, C.W. Chang, and Amos Tiao. Pablo White, a minority undergraduate, is also participating the project through an REU supplement.
Education and curriculum development: A textbook entitled ``Fuzzy Logic: Intelligence, Control, and Information'' (co-author: Reza Langari), will be published by Prentice Hall in mid 1998. The textbook is intended to be used for teaching upper-division undergraduate courses and/or beginning graduate courses on fuzzy logic, within the context of machine intelligence, control engineering, and information systems.
Department/institution infrastructure: The Center for Fuzzy Logic, Robotics, and Intelligent Systems has been established at Texas A&M University to promote interdisciplinary reserach and to facilitate technology transfer.
Industry: The Center has obtained industrial support from EXXON, CHEVERON, and other companies. A U.S. Patent for expert system technology has been obtained.

Due to this award, three major research activities have been enabled. First, an NSF project for investigating intelligent computing approaches for metabolic modeling started (with J. Liao) in 1995. Second, a project for developing intelligent requirement analysis techniques and tools was funded through the highly competitive Texas Advanced Research Program (ARP) in 1995. Finally, a project for applying the fuzzy modeling techniques developed in this project to reservoir modeling was funded by the Texas Advanced Technology Program (ATP) in 1997.

Project References

F. X. Liu and J. Yen, ``An Analytic Framework for Specifying and Analyzing Imprecise Requirements'', in Proceedings of 18th International Conference on Software Engineering (ICSE-18) , Berlin, Germany, pp 60-69, March 25-30, 1996.

J. Yen and W. Tiao, ``A Systematic Tradeoff Analysis for Conflicting Imprecise Requirements'', in Proceedings of the Third IEEE International Symposium on Requirements Engineering (RE'97), pp. 87-96, January 5-8, 1997.

J. Yen, X. Liu, and S. H. Teh, ``A Fuzzy Logic-based Methodology for the Acquisition and Analysis of Imprecise Requirements'', International Journal of Concurrent Engineering: Research & Applications , Special Issue on Applications of AI Techniques to Systems Engineering, Vol 2, No. 4, pp. 265-277, December 1994.