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2016 | OriginalPaper | Chapter

The Learnability of Business Rules

Authors : Olivier Wang, Changhai Ke, Leo Liberti, Christian de Sainte Marie

Published in: Machine Learning, Optimization, and Big Data

Publisher: Springer International Publishing

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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.

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Metadata
Title
The Learnability of Business Rules
Authors
Olivier Wang
Changhai Ke
Leo Liberti
Christian de Sainte Marie
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-51469-7_22

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