This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Optimizing Real-Time Equational Rule-Based Systems
February 2004 (vol. 30 no. 2)
pp. 112-125

Abstract—Analyzing and reducing the execution-time upper bound of real-time rule-based expert systems is a very important task because of the stringent timing constraints imposed on this class of systems. This paper presents a new runtime optimization to reduce the execution-time upper bound of real-time rule-based expert systems. In order to determine rules to be evaluated at runtime, a predicate dependency list, which consists of a predicate, its active rule set and corresponding inactive rule set, is created for each predicate in a real-time rule-based program. Based on the predicate dependency list and the current value of each variable, the new runtime optimization dynamically selects rules to be evaluated at runtime. For the timing analysis of the proposed algorithm, the paper introduces a predicate-based rule dependency graph, a predicate-based enable-rule graph, and their construction algorithm. The paper also discusses the bounded time of the equational logic rule-based program using the predicate-based rule dependency graph as well as the predicate-based enable-rule graph. The implementation and performance evaluation of the proposed algorithm using both synthetic and practical real-time rule-bases programs are also presented. The performance evaluation shows that the runtime optimizer reduces the number of rule evaluations and predicate evaluations as well as the response time upper bound significantly, and the new algorithm yields better execution-time upper bound compared to other optimization methods.

[1] J.-R. Chen and A.M.K. Cheng, Predicting the Response Time of Real-Time Rule-Based Programs with Variable-Expression Assignments Proc. Sixth IEEE CS Int'l Conf. Tools with Artificial Intelligence, pp. 297-303, 1994.
[2] J.-R. Chen and A.M.K. Cheng, Predicting the Response Time of OPS5-style Production Systems Proc. IEEE Conf. AI for Applications, Feb. 1995.
[3] J.-R. Chen and A.M.K. Cheng, “Response Time Analysis of EQL Real-Time Rule-Based Systems,” IEEE Trans. Knowledge and Data Eng., vol. 7, no. 1, pp. 26-43, Feb. 1995.
[4] A.M.K. Cheng, J.C. Browne, A.K. Mok, and R.-H. Wang, Estella: A Language for Specifying Behavioral Constraint Assertions in Real-Time Rule-Based Systems Proc. Sixth Ann. IEEE Conf. Computer Assurance, June 1991.
[5] A.M.K. Cheng, J.C. Browne, A.K. Mok, and R.-H. Wang, "Analysis of Real-Time Rule-Based System with Behavioral Constraint Assertions Specified in Estella," IEEE Trans. Software Eng., vol. 19, no. 9, pp. 863-885, Sept. 1993.
[6] A.M.K. Cheng, “Parallel Execution of Real-Time Rule-Based Systems,” Proc. Seventh Int'l Parallel Processing Symp., pp. 779-786, Apr. 1993.
[7] T. Ishida, “An Optimization Algorithm for Production Systems,” IEEE Trans. Knowledge and Data Eng., vol. 6, no. 4, pp. 549-558, Aug. 1994.
[8] D.P. Miranker and B.J. Lofaso,"The organization and performance of a TREAT-based production system compiler," IEEE Trans. Knowledge and Data Engineering, vol. 3, no. 1, pp. 3-10, Mar. 1991.
[9] D.P. Miranker, TREAT: A New and Efficient Match Algorithm for AI Production Systems Research Notes in AI, 1990.
[10] Y. Wang and E.N. Hanson,"A performance comparison of the Rete and TREAT algorithms for testing database rule conditions," Proc. IEEE Data Eng. Conf., pp. 88-97, Feb. 1992.
[11] A.J. Pasik, "A Source-to-Source Transformation for Increasing Rule-Based System Parallelism," IEEE Trans. Knowledge and Data Eng., vol. 4, no. 4, pp. 336-343, Aug. 1992.
[12] B. Zupan and A.M.K. Cheng, Optimization of Rule-Based Systems Using State Space Graphs IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, Apr. 1998.
[13] Y.-H. Lee and A.M.K. Cheng, Dynamic optimization for Real-Time Rule-Based Systems Using Predicate Dependency Proc. IEEE Real-Time Technology and Applications Symp., June 2000.
[14] M. Jarke, J. Mylopoulos, J.W. Schmidt, and Y. Vassiliou, "DAIDA: An Environment for Evolving Information Systems," ACM Trans. Information Systems, pp. 1-50, vol. 10, Jan. 1992.
[15] C.L. Forgy, RETE: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem Artificial Intelligence, vol. 19, pp. 17-37, 1982.
[16] D.P. Miranker and D.A. Brant,"An algorithmic basis for integrating production systems and large databases," Proc. IEEE Data Eng. Conf., pp. 353-360, 1990.
[17] J.C. Browne, A.M.K. Cheng, and A.K. Mok, Computer-Aided Design of Real-Time Rule-Based Decision Systems technical report, Computer Science Dept., Univ. of Texas at Austin, 1988.
[18] R.-H. Wang and A.K. Mok, Response-Time Bounds of Rule-Based Programs Under Rule Priority Structure Proc. IEEE Real-Time Systems Symp., pp. 142-151, 1994.
[19] M. Jarke and J. Koch, Query Optimization in Database Systems Computing Surveys, vol. 16, no. 2, June 1984.
[20] M. Cherniack and S. Zdonik, Changing the Rules: Transformations for Rule-Based Optimizers Proc. ACM SIGMOD, June 1998.
[21] A.J. Gonzalez and D.D. Dankel, The Engineering of Knowledge-Based Systems. Prentice Hall, 1993.
[22] J.J. Helly, Distributed Expert System for Space Shuttle Flight Control PhD dissertation, Dept. of Computer Science, Univ. of Calif. at Los Aangeles, 1984.
[23] C.A. Marsh, The ISA Expert System: A Prototype System for Failure Diagnosis on the Space Station Mitre report, The MITRE Corporation, Houston, 1988.

Index Terms:
EQL language, graphs, real-time rule-based systems, runtime optimization.
Citation:
Yun-Hong Lee, Albert Mo Kim Cheng, "Optimizing Real-Time Equational Rule-Based Systems," IEEE Transactions on Software Engineering, vol. 30, no. 2, pp. 112-125, Feb. 2004, doi:10.1109/TSE.2004.1265816
Usage of this product signifies your acceptance of the Terms of Use.