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DeterministicSchedsParetoExplorer: Selecting LP-based weight vector checkers in case of properties that are not supported by the standard weight vector checker.

tempestpy_adaptions
TimQu 6 years ago
parent
1a4fe91797
commit
621aae1c4c
3 changed files with 41 additions and 19 deletions
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  1. 49
      src/storm/modelchecker/multiobjective/deterministicScheds/DeterministicSchedsParetoExplorer.cpp
  2. 1
      src/storm/modelchecker/multiobjective/pcaa/StandardPcaaWeightVectorChecker.h
  3. 10
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessorResult.h

49
src/storm/modelchecker/multiobjective/deterministicScheds/DeterministicSchedsParetoExplorer.cpp
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@ -283,7 +283,11 @@ namespace storm {
objectiveHelper.emplace_back(*model, obj);
}
lpChecker = std::make_shared<DeterministicSchedsLpChecker<SparseModelType, GeometryValueType>>(env, *model, objectiveHelper);
wvChecker = storm::modelchecker::multiobjective::WeightVectorCheckerFactory<SparseModelType>::create(preprocessorResult);
if (preprocessorResult.containsOnlyTotalRewardFormulas()) {
wvChecker = storm::modelchecker::multiobjective::WeightVectorCheckerFactory<SparseModelType>::create(preprocessorResult);
} else {
wvChecker = nullptr;
}
}
template <class SparseModelType, typename GeometryValueType>
@ -395,14 +399,17 @@ namespace storm {
for (uint64_t objIndex = 0; objIndex < objectives.size(); ++objIndex) {
std::vector<GeometryValueType> weightVector(objectives.size(), storm::utility::zero<ModelValueType>());
weightVector[objIndex] = storm::utility::one<GeometryValueType>();
//negateMinObjectives(weightVector);
wvChecker->check(env, storm::utility::vector::convertNumericVector<ModelValueType>(weightVector));
auto point = storm::utility::vector::convertNumericVector<GeometryValueType>(wvChecker->getUnderApproximationOfInitialStateResults());
//lpChecker->setCurrentWeightVector(weightVector);
//auto point = lpChecker->check(env, negateMinObjectives(this->overApproximation));
//STORM_LOG_THROW(point.is_initialized(), storm::exceptions::UnexpectedException, "Unable to find a point in the current overapproximation.");
//negateMinObjectives(weightVector);
negateMinObjectives(point);
std::vector<GeometryValueType> point;
if (wvChecker) {
wvChecker->check(env, storm::utility::vector::convertNumericVector<ModelValueType>(weightVector));
point = storm::utility::vector::convertNumericVector<GeometryValueType>(wvChecker->getUnderApproximationOfInitialStateResults());
negateMinObjectives(point);
} else {
lpChecker->setCurrentWeightVector(weightVector);
auto optionalPoint = lpChecker->check(env, negateMinObjectives(this->overApproximation));
STORM_LOG_THROW(optionalPoint.is_initialized(), storm::exceptions::UnexpectedException, "Unable to find a point in the current overapproximation.");
point = std::move(optionalPoint.get());
}
Point p(point);
p.setOnFacet();
// Adapt the overapproximation
@ -483,20 +490,26 @@ namespace storm {
// Invoke optimization and insert the explored points
boost::optional<PointId> optPointId;
wvChecker->check(env, storm::utility::vector::convertNumericVector<ModelValueType>(f.getHalfspace().normalVector()));
auto point = storm::utility::vector::convertNumericVector<GeometryValueType>(wvChecker->getUnderApproximationOfInitialStateResults());
//auto currentArea = negateMinObjectives(overApproximation->intersection(f.getHalfspace().invert()));
//auto point = lpChecker->check(env, currentArea);
// if (point.is_initialized()) {
negateMinObjectives(point);
std::vector<GeometryValueType> point;
if (wvChecker) {
wvChecker->check(env, storm::utility::vector::convertNumericVector<ModelValueType>(f.getHalfspace().normalVector()));
point = storm::utility::vector::convertNumericVector<GeometryValueType>(wvChecker->getUnderApproximationOfInitialStateResults());
negateMinObjectives(point);
} else {
auto currentArea = negateMinObjectives(overApproximation->intersection(f.getHalfspace().invert()));
auto optionalPoint = lpChecker->check(env, currentArea);
if (optionalPoint.is_initialized()) {
point = std::move(optionalPoint.get());
} else {
// As we did not find any feasable solution in the given area, we take a point that lies on the facet
point = pointset.getPoint(f.getPoints().front()).get();
}
}
Point p(point);
p.setOnFacet();
GeometryValueType offset = storm::utility::vector::dotProduct(f.getHalfspace().normalVector(), p.get());
addHalfspaceToOverApproximation(env, f.getHalfspace().normalVector(), offset);
optPointId = pointset.addPoint(env, std::move(p));
//} else {
// addHalfspaceToOverApproximation(env, f.getHalfspace().normalVector(), f.getHalfspace().offset());
//}
// Potentially generate new facets
if (optPointId) {

1
src/storm/modelchecker/multiobjective/pcaa/StandardPcaaWeightVectorChecker.h
View File

@ -34,7 +34,6 @@ namespace storm {
* @param possibleBottomStates The states for which it is posible to not collect further reward with prob. 1
*
*/
StandardPcaaWeightVectorChecker(preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult);
/*!

10
src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessorResult.h
View File

@ -38,6 +38,16 @@ namespace storm {
// Intentionally left empty
}
bool containsOnlyTotalRewardFormulas() const {
for (auto const& obj : objectives) {
if (!obj.formula->isRewardOperatorFormula() || !obj.formula->getSubformula().isTotalRewardFormula()) {
return false;
}
}
return true;
}
bool containsOnlyTrivialObjectives() const {
// Trivial objectives are either total reward formulas or single-dimensional step or time bounded cumulative reward formulas
for (auto const& obj : objectives) {

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