Abstract
Among programming languages, a popular one in corporate environments is Business Rules. These are conditional statements which can be seen as a sort of “programming for non-programmers”, since they remove loops and function calls, which are typically the most difficult programming constructs to master by laypeople. A Business Rules program consists of a sequence of “IF condition THEN actions” statements. Conditions are verified over a set of variables, and actions assign new values to the variables. Medium-sized to large corporations often enforce, document and define their business processes by means of Business Rules programs. Such programs are executed in a special purpose virtual machine which verifies conditions and executes actions in an implicit loop. A problem of extreme interest in business environments is enforcing high-level strategic decisions by configuring the parameters of Business Rules programs so that they behave in a certain prescribed way on average. In this paper we show that Business Rules are Turing-complete. As a consequence, we argue that there can exist no algorithm for configuring the average behavior of all possible Business Rules programs.
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
Similar content being viewed by others
References
Berliner, L.M.: Statistics, probability and chaos. Stat. Sci. 7, 69–90 (1992)
Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1), 285–297 (1998)
Cohen, A., Goldwasser, S., Vaikuntanathan, V.: Aggregate pseudorandom functions and connections to learning. In: Dodis, Y., Nielsen, J.B. (eds.) TCC 2015. LNCS, vol. 9015, pp. 61–89. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46497-7_3
de Sainte Marie, C., Hallmark, G., Paschke, A.: RIF Production Rule Dialect, 2nd edn. W3C Recommendation (2013)
Goldreich, O., Goldwasser, S., Micali, S.: How to construct random functions. J. ACM 4(33), 792–807 (1986)
Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Boston (1979)
IBM. Operational Decision Manager 8.8 (2015)
Kepser, S.: A simple proof of the turing-completeness of XSLT and XQuery. In: Usdin, T. (ed.) Extreme Markup Languages 2004 (2004)
Kleene, S.C.: Introduction to Metamathematics. North-Holland Publishing Co., Amsterdam (1952)
Liberti, L., Marinelli, F.: Mathematical programming: turing completeness and applications to software analysis. J. Comb. Optim. 28(1), 82–104 (2014)
De Raedt, L., Džeroski, S.: First-order jk-clausal theories are PAC-learnable. Artif. Intell. 104(1), 375–392 (1994)
Shannon, C.: A Universal Turing machine with two internal states. In: Shannon, C., McCarthy, J. (eds.) Automata Studies. Annals of Mathematics Studies, vol. 34, pp. 157–165. Princeton University Press, Princeton (1956)
Turing, A.: On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. 42(1), 230–265 (1937)
Valiant, L.G.: A theory of the learnable. Commun. ACM 11(27), 1134–1142 (1984)
Wang, O., Kai, C., Liberti, L., De Sainte Marie, C.: Business rule sets as programs: turing-completeness and structural operational semantics. In: Treizièmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA 2015) (2015)
Wang, O., Liberti, L., D’Ambrosio, C., Sainte Marie, C., Ke, C.: Controlling the average behavior of business rules programs. In: Alferes, J.J.J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds.) RuleML 2016. LNCS, vol. 9718, pp. 83–96. Springer, Heidelberg (2016). doi:10.1007/978-3-319-42019-6_6
Acknowledgments
The first author (OW) is supported by an IBM France/ANRT CIFRE Ph.D. thesis award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, O., Ke, C., Liberti, L., de Sainte Marie, C. (2016). The Learnability of Business Rules. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-51469-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51468-0
Online ISBN: 978-3-319-51469-7
eBook Packages: Computer ScienceComputer Science (R0)