@inproceedings{b0411aaf14564ade8e5b0ba1504e9a83,
title = "Controlling the average behavior of business rules programs",
abstract = "Business Rules are a programming paradigm for nonprogrammer 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.",
author = "Olivier Wang and Leo Liberti and Claudia D{\textquoteright}Ambrosio and Marie, \{Christian de Sainte\} and Changhai Ke",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 10th International Symposium on Rule Technologies, RuleML 2016 ; Conference date: 06-07-2016 Through 09-07-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-42019-6\_6",
language = "English",
isbn = "9783319420189",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "83--96",
editor = "Dumitru Roman and Alferes, \{Jose Julio\} and Paul Fodor and Leopoldo Bertossi and Guido Governatori",
booktitle = "Rule Technologies",
}