Abstract
Decision automation is expanding as many corporations capture and operate their business policies through business rules. Because laws and corporate regulations require transparency, decision automation must also provide some explanation capabilities. Most rule engines provide information about the rules that are executed, but rarely give an explanation about why those rules executed without degrading their performance. A need exists for a human readable decision trace that explains why decisions are made. This paper proposes a first approach to introduce causality to describe the existing (and sometimes hidden) relations in a decision trace of a Business Rule-Based System (BRBS). This involves a static analysis of the business rules and the construction of causal models.
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El Mernissi, K., Feillet, P., Maudet, N., Ouerdane, W. (2017). Introducing Causality in Business Rule-Based Decisions. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_47
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DOI: https://doi.org/10.1007/978-3-319-60042-0_47
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