Universality and prediction in business rules

Autor: Wang, Olivier, De Sainte Marie, Christian, Ke, Changhai, Liberti, Leo
Přispěvatelé: IBM France (IBM), IBM, Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Computational Intelligence
Computational Intelligence, Wiley, 2018, 34 (2), pp.763-785. ⟨10.1111/coin.12174⟩
ISSN: 0824-7935
1467-8640
DOI: 10.1111/coin.12174⟩
Popis: International audience; Business Rules have the form if condition then action. A Business Rules program, which can be executed by means of an interpreter, is a sequence of Business Rules. Motivated by IBM use cases, we look at the problem of setting parameter values in a given Business Rules 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 Business Rules programs. As a proof of concept, we show empirically that these can be used to detect potential non-termination situations.
Databáze: OpenAIRE
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