Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T12:28:01.088Z Has data issue: false hasContentIssue false
  • English
  • Français

Scaling up production systems: Issues, approaches and targets

Published online by Cambridge University Press:  07 July 2009

Anurag Acharya
Anurag Acharya
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213-3891, USA (email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Extract

Production systems have successfully made the transition from a trendy research idea to a routinely used programming paradigm. An important cause of this transition has been the several orders of magnitude speedup in program execution achieved in the past few years by the combination of better match algorithms, efficient compilation techniques and faster hardware platforms.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 9 , Issue 1 , March 1994 , pp. 67 - 72
Copyright
Copyright © Cambridge University Press 1994

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

Acharya, A, 1992. “Hashing alpha tests”. Unpublished note.Google Scholar
Acharya, A, 1993a. “PPL: An explicitly parallel language for production systems”. In: Proceedings IJCAI-93 Workshop on Production Systems and their Innovative Applications.Google Scholar
Acharya, A, 1993b. Recent unpublished results, 10.Google Scholar
Acharya, A and Tambe, M, 1993. “Collection-oriented match”. In: Proceedings IJCAI-93 Workshop on Production Systems and their Innovative Applications.CrossRefGoogle Scholar
Brant, D, Grose, T, Lofaso, B and Miranker, D, 1991. “Effects of database size on rule system performance. Five case studies”. In: Proceedings International Conference on Very Large Databases.Google Scholar
Brownston, L, Farrell, R, Kant, E and Martin, N, 1985. Programming Expert Systems with OPS5: An introduction to rule-based programming. Reading, MA: Addison-Wesley.Google Scholar
Carbonell, J, Knoblock, C and Minton, S, 1990. “PRODIGY: An integrated architecture for planning and learning”. In: Vanlehn, K (ed.), Architecture for Intelligence pp 241–278. Hillsdale, NJ: Erlbaum.Google Scholar
Cohen, WW, 1990. “Learning approximate rules of high utility”. In: Proceedings Sixth International Conference on Machine Learning pp 268–276.CrossRefGoogle Scholar
Delcambre, L and Etheredge, JN, 1988. “The relational production language: A production language for relational databases”. In: Proceedings Second International Conference on Expert Database Systems pp 333–352.Google Scholar
Doorenbos, R, 1992. Personal communication.Google Scholar
Doorenbos, R, Tambe, M and Newell, A, 1992. “Learning 10,000 chunks: What's it like out there?” In: Proceedings Tenth National Conference on Artificial Intelligence pp 830–836.Google Scholar
Doorenbos, R, 1993. “Matching 100,000 rules”. In: Proceedings Eleventh National Conference on Artificial Intelligence pp 290–296.Google Scholar
Etzioni, O, 1990. A structural theory of search control. PhD Thesis, School of Computer Science, Carnegie Mellon University.Google Scholar
Gratch, J and DeJong, G, 1992. “A probablistic solution to the utility problem in speed-up learning”. In: Proceedings Tenth National Conference on Artificial Intelligence pp 235–240.Google Scholar
Gupta, A, 1986. Parallelism in Production Systems. PhD Thesis, Computer Science Department, Carnegie Mellon University.Google Scholar
Gupta, A, Forgy, C, Newell, A and Wedig, R, 1986. “Parallel algorithms and architecture for production systems”. In: Proceedings Thirteenth International Symposium on Computer Architecture 06, pp 28–35.CrossRefGoogle Scholar
Hanson, E, 1992. “Rule condition testing and action execution in Ariel”. In: Proceedings SIGMOD Conference of Management of Data pp 49–58.CrossRefGoogle Scholar
Hanson, E and Widom, J, 1993. “An overview of production rules in database systems”. The Knowledge Engineering Review 8 (2) 06, 121–144.CrossRefGoogle Scholar
Harvey, W, 1993. Personal communication.Google Scholar
Hernandez, M and Stolfo, S, 1993. “Parallel programming of rule-based system in PARULEL”. In: Proceedings IJCAI-93 Workshop on Production Systems and their Innovatice Applications.Google Scholar
Laird, J, Newell, A and Rosenbloom, P, 1987. “Soar: An architecture for general intelligence”, Artificial Intelligence 33 (1) 1–64.CrossRefGoogle Scholar
Laffey, T, Weitzenkamp, S, Read, J, Kao, S and Schmidt, J, 1988. “Intelligent real-time monitoring”. In: Proceedings National Conference on Artificial Intelligence pp 72–76.Google Scholar
McKeown, DM, Harvey, WA and McDermott, J, 1985. “Rule based interpretation of aerial images”. IEEE Transactions on Pattern Analysis and Machine Intelligence 7 (5) 570–585.CrossRefGoogle Scholar
Minton, S, 1988. “Learning effective search control knowledge: An explanation-based approach”. PhD Thesis, Computer Science Department, Carnegie Mellon University.Google Scholar
Miranker, D, 1987. TREAT: A new and efficient match algorithm for AI production systems. San Mateo, CA: Morgan Kaufmann.Google Scholar
Miranker, D, Brant, D, Lofaso, B and Gadbois, D, 1990. “On the performance of lazy matching in production systems”. In: Proceedings Eighth National Conference on Artificial Intelligence pp 685–692.Google Scholar
Rim, Y and Cragon, H, 1988. “Multicache system simulation using a production system”. In: Proceedings Southeastern Simulation Conference, 10, pp 64–69.Google Scholar
Stolfo, S, 1991. Personal communication.Google Scholar
Stonebraker, M, 1992. “The integration of rule systems with database systems”. IEEE Transactions on Knowledge and Data Engineering 4 (5), 10, 415–423.CrossRefGoogle Scholar
Subramaniam, I, 1993. Managing discardable pages. PhD thesis, Department of Electrical and Computer Engineering, Carnegie Mellon University.Google Scholar
Tambe, M and Rosenbloom, P, 1990. “A framework for investigating production system formulations with polynomially bounded match”. Proceedings Eighth National Conference on Artificial Intelligence pp 693–700.Google Scholar
Widom, J and Finkelstein, S, 1989. “A syntax and semantics for set-oriented production rules in relational database systems”. SIGMOD Record, 18 (3), 09, 36–45.CrossRefGoogle Scholar
Zorn, B, 1989. Comparative performance evaluation of garbage collection algorithms. PhD Thesis, Computer Science Division, University of California Berkeley.Google Scholar