OpenRules RuleSolver is an open source tool that adds the power of Constraint and Linear Programming to Business Decision Modeling. It extends OpenRules Decision Manager to support Declarative Decision Modeling and Decision Optimization.

The newest OpenRules Release 10.1.0 comes with an essentially simplified RuleSolver which now requires only two tables “DefineVariables” and “PostConstraints“ to define many complex logical problems. It also provides predefined methods for problem resolution so you really may concentrate only on the question “WHAT” and not worry about “HOW”. I will demonstrate the new decision modeling facilities using a relatively complex logical problem “Family Riddle“.

These problem uses two simple tables “Define” (for Problem Definition) and “Solve” (for Problem Resolution):

Here are two tables that cover the problem definition:

The first column of these tables contains a plain English description of the described decision variable or constraint, and the second column contains names of the predefined methods used to defined them. Columns “Par 1”, “Par 2”, and “Par 3” (some of them optional) contain the parameters of these methods.

The tables “DefineVariables” and “PostConstraints” use 3 parameters for different methods such as AddVar, Sum, AddVarOperValue, AllDiff, and others. We may split these tables into several decision tables (called from the table “Define”) which may contain only 1, 2, or 3 parameters. It is easy to add more variable definition methods and constraint types.

When we execute this decision model, the predefined method “SolverFindAllSolutions” from the table “Define” will produce 

Thus, the decision model for this logical problem demonstrates the real Declarative Decision Modeling when we mainly concentrate on “WHAT” and not on “HOW”.

RuleSolver 10.1.0 comes with many samples of declarative decision models implemented using the described decision tables.

Note. So far the tables “DefineVariables” and “PostConstraints” require their parameters to be provided by decision model authors. In the future we plan automatically extract such parameters from the plain English formulations using the latest advances in the Natural Language Processing.