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Universality and prediction in business rules.

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Title: Universality and prediction in business rules.
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]
Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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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  Data: <i>Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2018
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