Abstract
Object-oriented Business Rules Management Systems (OO-BRMS) are a complex applications platform that provide tools for automating day-to-day business decisions. To allow more sophisticated and realistic decision-making, these tools must enable Business Rules (BRs) to handle uncertainties in the domain. For this purpose, several approaches have been proposed, but most of them rely on heuristic models that unfortunately have shortcomings and limitations. In this paper we present a solution allowing modern OO-BRMS to effectively integrate probabilistic reasoning for uncertainty management. This solution has a coupling approach with Probabilistic Relational Models (PRMs) and facilitates the inter-operability, hence, the separation between business and probabilistic logic. We apply our approach to an existing BRMS and discuss implications of the knowledge base dynamicity on the probabilistic inference.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For probability.
- 2.
- 3.
- 4.
The series of “if-then(-else)” statements.
- 5.
Actually, this automation is not always defined, but may require IT specialist insight.
- 6.
For instance, the risk of not executing a rule that should be executed (false negative).
- 7.
In general, one must specify how every language construct is compiled to be processed by this general operator and assure the preservation of queries operational semantic after the rewriting, but this is beyond the scope of this paper.
- 8.
See https://dslpitt.org/genie/wiki/JSMILE_and_Smile.NET for more details.
- 9.
Constrains on probability distribution.
References
Agli, H., Bonnard, P., Wuillemin, P., Gonzales, C.: Uncertain reasoning for business rules. In: Proceedings of the 8th International Web Rule Symposium Doctoral Consortium (2014)
Agli, H., Bonnard, P., Wuillemin, P., Gonzales, C.: Incremental junction tree inference. In: Proceedings of the 16th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference (2016, to appear)
Berstel-Da Silva, B.: Verification of Business Rules Programs. Springer, Heidelberg (2014)
Bobek, S., Nalepa, G.J.: Compact representation of conditional probability for rule-based mobile context-aware systems. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 83–96. Springer, Heidelberg (2015)
Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)
De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)
Elkan, C.: The paradoxical success of fuzzy logic. In: IEEE Expert, pp. 698–703 (1993)
Forgy, C.L.: RETE: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19(1), 17–37 (1982)
Graham, I.: Business Rules Management and Service Oriented Architecture: A Pattern Language. Wiley, Chichester (2006)
Hart, P.E., Duda, R.O., Einaudi, M.T.: PROSPECTOR–a computer-based consultation system for mineral exploration. J. Int. Assoc. Math. Geol. 10(5), 589–610 (1977)
Heckerman, D.E., Shortliffe, E.H.: From certainty factors to belief networks. Artif. Intell. Med. 4(1), 35–52 (1992)
Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI), pp. 580–587 (1998)
Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 302–313 (1997)
Korver, M., Lucas, P.J.F.: Converting a rule-based expert system into a belief network. Med. Informatics 18, 219–241 (1993)
Mahoney, S.M., Laskey, K.B.: Network engineering for complex belief networks. In: Proceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence (UAI), pp. 389–396 (1996)
Ng, K.C., Abramson, B.: Uncertainty management in expert systems. IEEE Expert Intell. Syst. Appl. 5(2), 29–48 (1990)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Pfeffer, A.J.: Probabilistic Reasoning for Complex Systems. Ph.D. thesis, Stanford University (2000)
Torti, L., Gonzales, C., Wuillemin, P.H.: Speeding-up structured probabilistic inference using pattern mining. Int. J. Approximate Reasoning 54(7), 900–918 (2013)
Wuillemin, P.H., Torti, L.: Structured probabilistic inference. Int. J. Approximate Reasoning 53(7), 946–968 (2012)
Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965)
Acknowledgments
This work was partially supported by IBM France Lab/ANRT CIFRE under the grant #421/2014. The authors would like to thank Christian De Sainte Marie for useful discussions and insights.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Agli, H., Bonnard, P., Gonzales, C., Wuillemin, PH. (2016). Business Rules Uncertainty Management with Probabilistic Relational Models. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-42019-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42018-9
Online ISBN: 978-3-319-42019-6
eBook Packages: Computer ScienceComputer Science (R0)