Universality and prediction in business rules.
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| Title: | Universality and prediction in business rules. |
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| Authors: | Wang, Olivier1, de Sainte Marie, Christian2, Ke, Changhai2, Liberti, Leo1 liberti@lix.polytechnique.fr |
| Source: | Computational Intelligence. May2018, Vol. 34 Issue 2, p763-785. 23p. |
| Subjects: | Interpreters (Computer programs), Machine learning, Semantics, Artificial intelligence, International Business Machines Corp. |
| Abstract: | Abstract: Business rules (BR) have the form ⟨ if condition then action⟩. A BR program, which can be executed by means of an interpreter, is a sequence of business rules. Motivated by International Business Machines use cases, we look at the problem of setting parameter values in a given BR program so it will achieve a given average goal over all possible instances. We explore the following fundamental question: Is there a general learning algorithm, which addresses this issue? We prove the answer is negative. On the positive side, we derive operational semantics for BR programs. As a proof of concept, we show empirically that these can be used to detect potential nontermination situations. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Abstract: Business rules (BR) have the form ⟨ if condition then action⟩. A BR program, which can be executed by means of an interpreter, is a sequence of business rules. Motivated by International Business Machines use cases, we look at the problem of setting parameter values in a given BR program so it will achieve a given average goal over all possible instances. We explore the following fundamental question: Is there a general learning algorithm, which addresses this issue? We prove the answer is negative. On the positive side, we derive operational semantics for BR programs. As a proof of concept, we show empirically that these can be used to detect potential nontermination situations. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 08247935 |
| DOI: | 10.1111/coin.12174 |
