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modelchecker: Added support for non-trivial reward accumulations for Sparse/Hybrid/Dd engines.

tempestpy_adaptions
TimQu 6 years ago
parent
c43e13172f
commit
c7aec92dc9
10 changed files with 139 additions and 39 deletions
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  1. 13
      src/storm/modelchecker/csl/HybridCtmcCslModelChecker.cpp
  2. 16
      src/storm/modelchecker/csl/SparseCtmcCslModelChecker.cpp
  3. 12
      src/storm/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.cpp
  4. 12
      src/storm/modelchecker/prctl/HybridDtmcPrctlModelChecker.cpp
  5. 9
      src/storm/modelchecker/prctl/HybridMdpPrctlModelChecker.cpp
  6. 16
      src/storm/modelchecker/prctl/SparseDtmcPrctlModelChecker.cpp
  7. 16
      src/storm/modelchecker/prctl/SparseMdpPrctlModelChecker.cpp
  8. 9
      src/storm/modelchecker/prctl/SymbolicDtmcPrctlModelChecker.cpp
  9. 9
      src/storm/modelchecker/prctl/SymbolicMdpPrctlModelChecker.cpp
  10. 64
      src/storm/utility/FilteredRewardModel.h

13
src/storm/modelchecker/csl/HybridCtmcCslModelChecker.cpp
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@ -10,6 +10,7 @@
#include "storm/storage/dd/DdManager.h"
#include "storm/storage/dd/Add.h"
#include "storm/storage/dd/Bdd.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/logic/FragmentSpecification.h"
@ -31,7 +32,7 @@ namespace storm {
template<typename CValueType, typename std::enable_if<storm::NumberTraits<CValueType>::SupportsExponential, int>::type>
bool HybridCtmcCslModelChecker<ModelType>::canHandleImplementation(CheckTask<storm::logic::Formula, CValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
return formula.isInFragment(storm::logic::csrl().setGloballyFormulasAllowed(false).setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeAllowed(true).setTimeOperatorsAllowed(true));
return formula.isInFragment(storm::logic::csrl().setGloballyFormulasAllowed(false).setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeAllowed(true).setTimeOperatorsAllowed(true).setRewardAccumulationAllowed(true));
}
template <typename ModelType>
@ -66,8 +67,8 @@ namespace storm {
storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
SymbolicQualitativeCheckResult<DdType> const& subResult = subResultPointer->asSymbolicQualitativeCheckResult<DdType>();
return storm::modelchecker::helper::HybridCtmcCslHelper::computeReachabilityRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridCtmcCslHelper::computeReachabilityRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
}
template<typename ModelType>
@ -117,7 +118,8 @@ namespace storm {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(rewardPathFormula.getTimeBoundReference().isTimeBound(), storm::exceptions::NotImplementedException, "Currently step-bounded and reward-bounded properties on CTMCs are not supported.");
return storm::modelchecker::helper::HybridCtmcCslHelper::computeCumulativeRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<double>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridCtmcCslHelper::computeCumulativeRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), rewardModel.get(), rewardPathFormula.getNonStrictBound<double>());
}
template<typename ModelType>
@ -131,7 +133,8 @@ namespace storm {
template<typename ModelType>
std::unique_ptr<CheckResult> HybridCtmcCslModelChecker<ModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
return storm::modelchecker::helper::HybridCtmcCslHelper::computeLongRunAverageRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""));
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridCtmcCslHelper::computeLongRunAverageRewards<DdType, ValueType>(env, this->getModel(), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), rewardModel.get());
}
// Explicitly instantiate the model checker.

16
src/storm/modelchecker/csl/SparseCtmcCslModelChecker.cpp
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@ -9,6 +9,7 @@
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/utility/solver.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
@ -37,7 +38,7 @@ namespace storm {
template<typename CValueType, typename std::enable_if<storm::NumberTraits<CValueType>::SupportsExponential, int>::type>
bool SparseCtmcCslModelChecker<SparseCtmcModelType>::canHandleImplementation(CheckTask<storm::logic::Formula, CValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
return formula.isInFragment(storm::logic::csrl().setGloballyFormulasAllowed(false).setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeAllowed(true).setTotalRewardFormulasAllowed(true));
return formula.isInFragment(storm::logic::csrl().setGloballyFormulasAllowed(false).setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeAllowed(true).setTotalRewardFormulasAllowed(true).setRewardAccumulationAllowed(true));
}
template <typename SparseCtmcModelType>
@ -103,7 +104,8 @@ namespace storm {
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<SparseCtmcModelType>::computeCumulativeRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::CumulativeRewardFormula, ValueType> const& checkTask) {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(rewardPathFormula.getTimeBoundReference().isTimeBound(), storm::exceptions::NotImplementedException, "Currently step-bounded and reward-bounded properties on CTMCs are not supported.");
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<double>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector(), rewardModel.get(), rewardPathFormula.getNonStrictBound<double>());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
@ -112,14 +114,15 @@ namespace storm {
storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRateVector(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
template<typename SparseCtmcModelType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<SparseCtmcModelType>::computeTotalRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::TotalRewardFormula, ValueType> const& checkTask) {
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRateVector(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRateVector(), rewardModel.get(), checkTask.isQualitativeSet());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
@ -137,7 +140,8 @@ namespace storm {
template <typename SparseCtmcModelType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<SparseCtmcModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
storm::storage::SparseMatrix<ValueType> probabilityMatrix = storm::modelchecker::helper::SparseCtmcCslHelper::computeProbabilityMatrix(this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector());
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeLongRunAverageRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), probabilityMatrix, checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), &this->getModel().getExitRateVector());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeLongRunAverageRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), probabilityMatrix, rewardModel.get(), &this->getModel().getExitRateVector());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}

12
src/storm/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.cpp
View File

@ -6,6 +6,7 @@
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/utility/macros.h"
#include "storm/settings/SettingsManager.h"
@ -35,7 +36,7 @@ namespace storm {
template<typename CValueType, typename std::enable_if<storm::NumberTraits<CValueType>::SupportsExponential, int>::type>
bool SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::canHandleImplementation(CheckTask<storm::logic::Formula, CValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
if(formula.isInFragment(storm::logic::csl().setGloballyFormulasAllowed(false).setNextFormulasAllowed(false).setRewardOperatorsAllowed(true).setReachabilityRewardFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setTimeAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setLongRunAverageRewardFormulasAllowed(true))) {
if(formula.isInFragment(storm::logic::csl().setGloballyFormulasAllowed(false).setNextFormulasAllowed(false).setRewardOperatorsAllowed(true).setReachabilityRewardFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setTimeAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setLongRunAverageRewardFormulasAllowed(true).setRewardAccumulationAllowed(true))) {
return true;
} else {
// Check whether we consider a multi-objective formula
@ -106,7 +107,8 @@ namespace storm {
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), rewardModel.get(), subResult.getTruthValuesVector());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
}
@ -115,7 +117,8 @@ namespace storm {
std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeTotalRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::TotalRewardFormula, ValueType> const& checkTask) {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
STORM_LOG_THROW(this->getModel().isClosed(), storm::exceptions::InvalidPropertyException, "Unable to compute reachability rewards in non-closed Markov automaton.");
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeTotalRewards(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""));
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeTotalRewards(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), rewardModel.get());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
}
@ -136,7 +139,8 @@ namespace storm {
std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
STORM_LOG_THROW(this->getModel().isClosed(), storm::exceptions::InvalidPropertyException, "Unable to compute long run average rewards in non-closed Markov automaton.");
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeLongRunAverageRewards<ValueType, RewardModelType>(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getUniqueRewardModel());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeLongRunAverageRewards<ValueType, RewardModelType>(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), rewardModel.get());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
}

12
src/storm/modelchecker/prctl/HybridDtmcPrctlModelChecker.cpp
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@ -8,6 +8,7 @@
#include "storm/utility/macros.h"
#include "storm/utility/graph.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/models/symbolic/StandardRewardModel.h"
@ -33,7 +34,7 @@ namespace storm {
template<typename ModelType>
bool HybridDtmcPrctlModelChecker<ModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true));
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true));
}
template<typename ModelType>
@ -78,7 +79,8 @@ namespace storm {
std::unique_ptr<CheckResult> HybridDtmcPrctlModelChecker<ModelType>::computeCumulativeRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::CumulativeRewardFormula, ValueType> const& checkTask) {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
}
template<typename ModelType>
@ -93,7 +95,8 @@ namespace storm {
storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
SymbolicQualitativeCheckResult<DdType> const& subResult = subResultPointer->asSymbolicQualitativeCheckResult<DdType>();
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
}
template<typename ModelType>
@ -117,7 +120,8 @@ namespace storm {
template<typename ModelType>
std::unique_ptr<CheckResult> HybridDtmcPrctlModelChecker<ModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeLongRunAverageRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""));
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridDtmcPrctlHelper<DdType, ValueType>::computeLongRunAverageRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get());
}
template class HybridDtmcPrctlModelChecker<storm::models::symbolic::Dtmc<storm::dd::DdType::CUDD, double>>;

9
src/storm/modelchecker/prctl/HybridMdpPrctlModelChecker.cpp
View File

@ -19,6 +19,7 @@
#include "storm/modelchecker/multiobjective/multiObjectiveModelChecking.h"
#include "storm/solver/MinMaxLinearEquationSolver.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/settings/modules/GeneralSettings.h"
@ -39,7 +40,7 @@ namespace storm {
template<typename ModelType>
bool HybridMdpPrctlModelChecker<ModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true))) {
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true))) {
return true;
} else {
// Check whether we consider a multi-objective formula
@ -98,7 +99,8 @@ namespace storm {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
return storm::modelchecker::helper::HybridMdpPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridMdpPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
}
template<typename ModelType>
@ -115,7 +117,8 @@ namespace storm {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
SymbolicQualitativeCheckResult<DdType> const& subResult = subResultPointer->asSymbolicQualitativeCheckResult<DdType>();
return storm::modelchecker::helper::HybridMdpPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::HybridMdpPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
}
template<typename ModelType>

16
src/storm/modelchecker/prctl/SparseDtmcPrctlModelChecker.cpp
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@ -4,6 +4,7 @@
#include <memory>
#include "storm/utility/macros.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
@ -33,7 +34,7 @@ namespace storm {
template<typename SparseDtmcModelType>
bool SparseDtmcPrctlModelChecker<SparseDtmcModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setConditionalProbabilityFormulasAllowed(true).setConditionalRewardFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setOnlyEventuallyFormuluasInConditionalFormulasAllowed(true).setRewardBoundedUntilFormulasAllowed(true).setRewardBoundedCumulativeRewardFormulasAllowed(true).setMultiDimensionalBoundedUntilFormulasAllowed(true).setMultiDimensionalCumulativeRewardFormulasAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true))) {
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setConditionalProbabilityFormulasAllowed(true).setConditionalRewardFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setOnlyEventuallyFormuluasInConditionalFormulasAllowed(true).setRewardBoundedUntilFormulasAllowed(true).setRewardBoundedCumulativeRewardFormulasAllowed(true).setMultiDimensionalBoundedUntilFormulasAllowed(true).setMultiDimensionalCumulativeRewardFormulasAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true))) {
return true;
} else if (formula.isInFragment(storm::logic::quantiles())) {
if (this->getModel().getInitialStates().getNumberOfSetBits() > 1) return false;
@ -101,6 +102,7 @@ namespace storm {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
if (rewardPathFormula.isMultiDimensional() || rewardPathFormula.getTimeBoundReference().isRewardBound()) {
STORM_LOG_THROW(checkTask.isOnlyInitialStatesRelevantSet(), storm::exceptions::InvalidOperationException, "Checking non-trivial bounded until probabilities can only be computed for the initial states of the model.");
STORM_LOG_THROW(checkTask.getFormula().hasRewardAccumulation(), storm::exceptions::InvalidOperationException, "Checking reward bounded cumulative reward formulas is not supported if reward accumulations are given.");
storm::logic::OperatorInformation opInfo;
if (checkTask.isBoundSet()) {
opInfo.bound = checkTask.getBound();
@ -110,7 +112,8 @@ namespace storm {
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
} else {
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
}
@ -128,7 +131,8 @@ namespace storm {
storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet(), checkTask.getHint());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet(), checkTask.getHint());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
@ -143,7 +147,8 @@ namespace storm {
template<typename SparseDtmcModelType>
std::unique_ptr<CheckResult> SparseDtmcPrctlModelChecker<SparseDtmcModelType>::computeTotalRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::TotalRewardFormula, ValueType> const& checkTask) {
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), checkTask.isQualitativeSet(), checkTask.getHint());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), rewardModel.get(), checkTask.isQualitativeSet(), checkTask.getHint());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
@ -158,7 +163,8 @@ namespace storm {
template<typename SparseDtmcModelType>
std::unique_ptr<CheckResult> SparseDtmcPrctlModelChecker<SparseDtmcModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeLongRunAverageRewards<ValueType>(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), nullptr);
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseCtmcCslHelper::computeLongRunAverageRewards<ValueType>(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), rewardModel.get(), nullptr);
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}

16
src/storm/modelchecker/prctl/SparseMdpPrctlModelChecker.cpp
View File

@ -4,6 +4,7 @@
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
@ -40,7 +41,7 @@ namespace storm {
template<typename SparseMdpModelType>
bool SparseMdpPrctlModelChecker<SparseMdpModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setConditionalProbabilityFormulasAllowed(true).setOnlyEventuallyFormuluasInConditionalFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setRewardBoundedUntilFormulasAllowed(true).setRewardBoundedCumulativeRewardFormulasAllowed(true).setMultiDimensionalBoundedUntilFormulasAllowed(true).setMultiDimensionalCumulativeRewardFormulasAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true))) {
if (formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setConditionalProbabilityFormulasAllowed(true).setOnlyEventuallyFormuluasInConditionalFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setRewardBoundedUntilFormulasAllowed(true).setRewardBoundedCumulativeRewardFormulasAllowed(true).setMultiDimensionalBoundedUntilFormulasAllowed(true).setMultiDimensionalCumulativeRewardFormulasAllowed(true).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true))) {
return true;
} else if (formula.isInFragment(storm::logic::multiObjective().setCumulativeRewardFormulasAllowed(true).setTimeBoundedCumulativeRewardFormulasAllowed(true).setStepBoundedCumulativeRewardFormulasAllowed(true).setRewardBoundedCumulativeRewardFormulasAllowed(true).setTimeBoundedUntilFormulasAllowed(true).setStepBoundedUntilFormulasAllowed(true).setRewardBoundedUntilFormulasAllowed(true).setMultiDimensionalBoundedUntilFormulasAllowed(true).setMultiDimensionalCumulativeRewardFormulasAllowed(true))) {
// Check whether we consider a multi-objective formula
@ -140,6 +141,7 @@ namespace storm {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
if (rewardPathFormula.isMultiDimensional() || rewardPathFormula.getTimeBoundReference().isRewardBound()) {
STORM_LOG_THROW(checkTask.isOnlyInitialStatesRelevantSet(), storm::exceptions::InvalidOperationException, "Checking reward bounded cumulative reward formulas can only be done for the initial states of the model.");
STORM_LOG_THROW(checkTask.getFormula().hasRewardAccumulation(), storm::exceptions::InvalidOperationException, "Checking reward bounded cumulative reward formulas is not supported if reward accumulations are given.");
STORM_LOG_WARN_COND(!checkTask.isQualitativeSet(), "Checking reward bounded until formulas is not optimized w.r.t. qualitative queries");
storm::logic::OperatorInformation opInfo(checkTask.getOptimizationDirection());
if (checkTask.isBoundSet()) {
@ -151,7 +153,8 @@ namespace storm {
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
} else {
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeCumulativeRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
}
}
@ -171,7 +174,8 @@ namespace storm {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
auto ret = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet(), checkTask.isProduceSchedulersSet(), checkTask.getHint());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
auto ret = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet(), checkTask.isProduceSchedulersSet(), checkTask.getHint());
std::unique_ptr<CheckResult> result(new ExplicitQuantitativeCheckResult<ValueType>(std::move(ret.values)));
if (checkTask.isProduceSchedulersSet() && ret.scheduler) {
result->asExplicitQuantitativeCheckResult<ValueType>().setScheduler(std::move(ret.scheduler));
@ -196,7 +200,8 @@ namespace storm {
template<typename SparseMdpModelType>
std::unique_ptr<CheckResult> SparseMdpPrctlModelChecker<SparseMdpModelType>::computeTotalRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::TotalRewardFormula, ValueType> const& checkTask) {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
auto ret = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), checkTask.isQualitativeSet(), checkTask.isProduceSchedulersSet(), checkTask.getHint());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
auto ret = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeTotalRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), rewardModel.get(), checkTask.isQualitativeSet(), checkTask.isProduceSchedulersSet(), checkTask.getHint());
std::unique_ptr<CheckResult> result(new ExplicitQuantitativeCheckResult<ValueType>(std::move(ret.values)));
if (checkTask.isProduceSchedulersSet() && ret.scheduler) {
result->asExplicitQuantitativeCheckResult<ValueType>().setScheduler(std::move(ret.scheduler));
@ -217,7 +222,8 @@ namespace storm {
template<typename SparseMdpModelType>
std::unique_ptr<CheckResult> SparseMdpPrctlModelChecker<SparseMdpModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
std::vector<ValueType> result = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeLongRunAverageRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getUniqueRewardModel());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
std::vector<ValueType> result = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeLongRunAverageRewards(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), rewardModel.get());
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
}

9
src/storm/modelchecker/prctl/SymbolicDtmcPrctlModelChecker.cpp
View File

@ -5,6 +5,7 @@
#include "storm/storage/dd/Add.h"
#include "storm/utility/macros.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/models/symbolic/StandardRewardModel.h"
@ -30,7 +31,7 @@ namespace storm {
template<typename ModelType>
bool SymbolicDtmcPrctlModelChecker<ModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true));
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true));
}
template<typename ModelType>
@ -79,7 +80,8 @@ namespace storm {
std::unique_ptr<CheckResult> SymbolicDtmcPrctlModelChecker<ModelType>::computeCumulativeRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::CumulativeRewardFormula, ValueType> const& checkTask) {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
storm::dd::Add<DdType, ValueType> numericResult = storm::modelchecker::helper::SymbolicDtmcPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
storm::dd::Add<DdType, ValueType> numericResult = storm::modelchecker::helper::SymbolicDtmcPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
return std::unique_ptr<SymbolicQuantitativeCheckResult<DdType, ValueType>>(new SymbolicQuantitativeCheckResult<DdType, ValueType>(this->getModel().getReachableStates(), numericResult));
}
@ -96,7 +98,8 @@ namespace storm {
storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
SymbolicQualitativeCheckResult<DdType> const& subResult = subResultPointer->asSymbolicQualitativeCheckResult<DdType>();
storm::dd::Add<DdType, ValueType> numericResult = storm::modelchecker::helper::SymbolicDtmcPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
storm::dd::Add<DdType, ValueType> numericResult = storm::modelchecker::helper::SymbolicDtmcPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), subResult.getTruthValuesVector(), checkTask.isQualitativeSet());
return std::make_unique<SymbolicQuantitativeCheckResult<DdType, ValueType>>(this->getModel().getReachableStates(), numericResult);
}

9
src/storm/modelchecker/prctl/SymbolicMdpPrctlModelChecker.cpp
View File

@ -11,6 +11,7 @@
#include "storm/utility/macros.h"
#include "storm/utility/graph.h"
#include "storm/utility/FilteredRewardModel.h"
#include "storm/settings/modules/GeneralSettings.h"
@ -30,7 +31,7 @@ namespace storm {
template<typename ModelType>
bool SymbolicMdpPrctlModelChecker<ModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
storm::logic::Formula const& formula = checkTask.getFormula();
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true));
return formula.isInFragment(storm::logic::prctl().setLongRunAverageRewardFormulasAllowed(false).setTimeOperatorsAllowed(true).setReachbilityTimeFormulasAllowed(true).setRewardAccumulationAllowed(true));
}
template<typename ModelType>
@ -80,7 +81,8 @@ namespace storm {
storm::logic::CumulativeRewardFormula const& rewardPathFormula = checkTask.getFormula();
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidArgumentException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
STORM_LOG_THROW(rewardPathFormula.hasIntegerBound(), storm::exceptions::InvalidPropertyException, "Formula needs to have a discrete time bound.");
return storm::modelchecker::helper::SymbolicMdpPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), rewardPathFormula.getNonStrictBound<uint64_t>());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::SymbolicMdpPrctlHelper<DdType, ValueType>::computeCumulativeRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), rewardPathFormula.getNonStrictBound<uint64_t>());
}
template<typename ModelType>
@ -97,7 +99,8 @@ namespace storm {
STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
std::unique_ptr<CheckResult> subResultPointer = this->check(env, eventuallyFormula.getSubformula());
SymbolicQualitativeCheckResult<DdType> const& subResult = subResultPointer->asSymbolicQualitativeCheckResult<DdType>();
return storm::modelchecker::helper::SymbolicMdpPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), checkTask.isRewardModelSet() ? this->getModel().getRewardModel(checkTask.getRewardModel()) : this->getModel().getRewardModel(""), subResult.getTruthValuesVector());
auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
return storm::modelchecker::helper::SymbolicMdpPrctlHelper<DdType, ValueType>::computeReachabilityRewards(env, checkTask.getOptimizationDirection(), this->getModel(), this->getModel().getTransitionMatrix(), rewardModel.get(), subResult.getTruthValuesVector());
}
template<typename ModelType>

64
src/storm/utility/FilteredRewardModel.h
View File

@ -0,0 +1,64 @@
#pragma once
#include <memory>
#include "storm/utility/macros.h"
#include "storm/exceptions/NotSupportedException.h"
namespace storm {
namespace utility {
/*
* This class wraps around a
*/
template <typename RewardModelType>
class FilteredRewardModel {
public:
FilteredRewardModel(RewardModelType const& baseRewardModel, bool enableStateRewards, bool enableStateActionRewards, bool enableTransitionRewards) {
if ((baseRewardModel.hasStateRewards() && !enableStateRewards) || (baseRewardModel.hasStateActionRewards() && !enableStateActionRewards) || (baseRewardModel.hasTransitionRewards() && !enableTransitionRewards)) {
// One of the available reward types need to be deleted.
typename std::remove_const<typename std::remove_reference<decltype(baseRewardModel.getOptionalStateRewardVector())>::type>::type stateRewards;
if (enableStateRewards) {
stateRewards = baseRewardModel.getOptionalStateRewardVector();
}
typename std::remove_const<typename std::remove_reference<decltype(baseRewardModel.getOptionalStateActionRewardVector())>::type>::type stateActionRewards;
if (enableStateActionRewards) {
stateActionRewards = baseRewardModel.getOptionalStateActionRewardVector();
}
typename std::remove_const<typename std::remove_reference<decltype(baseRewardModel.getOptionalTransitionRewardMatrix())>::type>::type transitionRewards;
if (enableTransitionRewards) {
transitionRewards = baseRewardModel.getOptionalTransitionRewardMatrix();
}
localRewardModel = std::unique_ptr<RewardModelType>(new RewardModelType(stateRewards, stateActionRewards, transitionRewards));
rewardModel = localRewardModel.get();
} else {
rewardModel = &baseRewardModel;
}
}
RewardModelType const& get() const {
return *rewardModel;
}
private:
std::unique_ptr<RewardModelType> localRewardModel;
RewardModelType const* rewardModel;
};
template <typename ModelType, typename CheckTaskType>
FilteredRewardModel<typename ModelType::RewardModelType> createFilteredRewardModel(ModelType const& model, CheckTaskType const& checkTask) {
auto const& baseRewardModel = checkTask.isRewardModelSet() ? model.getRewardModel(checkTask.getRewardModel()) : model.getUniqueRewardModel();
if (checkTask.getFormula().hasRewardAccumulation()) {
auto const& acc = checkTask.getFormula().getRewardAccumulation();
STORM_LOG_THROW(model.isDiscreteTimeModel() || !acc.isExitSet() || !baseRewardModel.hasStateRewards(), storm::exceptions::NotSupportedException, "Exit rewards for continuous time models are not supported.");
// Check which of the available reward types are allowed.
bool hasStateRewards = model.isDiscreteTimeModel() ? acc.isExitSet() : acc.isTimeSet();
bool hasStateActionRewards = acc.isStepsSet();
bool hasTransitionRewards = acc.isStepsSet();
return FilteredRewardModel<typename ModelType::RewardModelType>(baseRewardModel, hasStateRewards, hasStateActionRewards, hasTransitionRewards);
} else {
return FilteredRewardModel<typename ModelType::RewardModelType>(baseRewardModel, true, true, true);
}
}
}
}
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