Universality and prediction in business rules. (30th April 2018)

• Record Type: Journal Article
• Authors: Wang, Olivier; de Sainte Marie, Christian; Ke, Changhai; Liberti, Leo
• Is Part Of: Computational intelligence. Volume 34:Number 2(2018)
• Page Start: 763
• Page End: 785
• Publication Date: 2018-04-30
• Subjects: business rules -- operational semantics -- statistical learning -- Turing‐completeness; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12174
• Languages: English
• ISSNs: 0824-7935

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.