private Assignment <TVar, TVal> RecursiveBacktrack(Assignment <TVar, TVal> currentAssignment, OptimizationProblem <TVar, TVal> problem, CancellationToken cancellationToken)
{
// This is based on an algorithm from
// "Artificial Intelligence: A Modern Approach" 2nd Edition by Stuart Russel and Perter Norvig (page 142).
if (currentAssignment.IsComplete(problem.Variables))
{
return(currentAssignment);
}
cancellationToken.ThrowIfCancellationRequested();
Variable <TVar, TVal> variable = VariableSelectionStrategy.SelectUnassignedVariable(problem.Variables, currentAssignment, new Problem <TVar, TVal>(problem));
IEnumerable <TVal> domain = DomainSortStrategy.GetOrderedDomain(variable, currentAssignment, new Problem <TVar, TVal>(problem));
foreach (var value in domain)
{
var nextAssignment = currentAssignment.Assign(variable, value);
if (this.costFunction.Cost(nextAssignment) >= this.best)
{
continue;
}
if (nextAssignment.IsConsistent(problem.Constraints))
{
var result = RecursiveBacktrack(nextAssignment, problem, cancellationToken);
if (result != null)
{
return(result);
}
}
}
return(null);
}
private Var SelectUnassignedVariable(CSP <Var, Val> csp, Assignment <Var, Val> assigment)
{
var vars = csp.Variables.Where(v => !assigment.Contains(v));
if (VariableSelectionStrategy != null)
{
vars = VariableSelectionStrategy.Apply(csp, vars);
}
return(vars.ElementAt(0));
}
internal static SelectionStrategy ForSolver(this VariableSelectionStrategy value) => value.ForSolver(VariableSelections);
public BacktrackSolver(InferenceStrategy <Tval> infs = null, VariableSelectionStrategy <Tval> vss = null, DomainValueSelectionStragety <Tval> dvss = null)
{
inferenceStrategy = infs ?? new AC3 <Tval>();
variableSelectionStrategy = vss ?? new MinimumRemainingValues <Tval>();
domainValueSelectionStragety = dvss ?? new UnorderedDomainValues <Tval>();
}
