Representing and Solving Rule-Based Decision Models with Constraint Solvers
- Cite this paper as:
- Feldman J. (2011) Representing and Solving Rule-Based Decision Models with Constraint Solvers. In: Olken F., Palmirani M., Sottara D. (eds) Rule-Based Modeling and Computing on the Semantic Web. Lecture Notes in Computer Science, vol 7018. Springer, Berlin, Heidelberg
This paper describes how constraint solvers could serve as rule engines in the context of modern business decision management systems. Decision models are based on rule families oriented to business users and frequently represented as Excel decision tables. The proposed approach uses exactly the same representation of decision models as a rule engine. The developed Rule Solver loads a decision model from multiple Excel files, generates a constraint satisfaction problem, and then validates it for consistency, diagnosing possible conflicts. Finally, it solves the problem, delivering results using the same terms as business rules. In fact, a user may switch between a rule engine and a constraint solver without changing the rules themselves. Additionally, Rule Solver can find solutions or find an optimal decision when business rules only partially define a problem. Rule Solver is implemented as an advanced component of the popular open source business decision management system “OpenRules”.
KeywordsDecision Model Rule Family Constraint Satisfaction Rule Engine Constraint Solver
Unable to display preview. Download preview PDF.