Abstract
Business Rules are a programming paradigm for non-programmer business users. They are designed to encode empirical knowledge of a business unit by means of “if-then” constructs. The classic example is that of a bank deciding whether to open a line of credit to a customer, depending on how the customer answers a list of questions. These questions are formulated by bank managers on the basis of the bank strategy and their own experience. Banks often have goals about target percentages of allowed loans. A natural question then arises: can the Business Rules be changed so as to meet that target on average? We tackle the question using “machine learning constrained” mathematical programs, which we solve using standard off-the-shelf solvers. We then generalize this to arbitrary decision problems.
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The first author (OW) is supported by an IBM France/ANRT CIFRE Ph.D. thesis award.
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Wang, O., Liberti, L., D’Ambrosio, C., de Sainte Marie, C., Ke, C. (2016). Controlling the Average Behavior of Business Rules Programs. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_6
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DOI: https://doi.org/10.1007/978-3-319-42019-6_6
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