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669 lines
52 KiB
669 lines
52 KiB
#include "src/modelchecker/reachability/SparseDtmcEliminationModelChecker.h"
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#include <algorithm>
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#include <chrono>
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#include "src/adapters/CarlAdapter.h"
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#include "src/settings/modules/SparseDtmcEliminationModelCheckerSettings.h"
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#include "src/settings/modules/GeneralSettings.h"
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#include "src/settings/SettingsManager.h"
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#include "src/storage/StronglyConnectedComponentDecomposition.h"
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#include "src/models/sparse/StandardRewardModel.h"
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#include "src/modelchecker/results/ExplicitQualitativeCheckResult.h"
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#include "src/modelchecker/results/ExplicitQuantitativeCheckResult.h"
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#include "src/utility/graph.h"
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#include "src/utility/vector.h"
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#include "src/utility/macros.h"
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#include "src/exceptions/InvalidPropertyException.h"
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#include "src/exceptions/InvalidStateException.h"
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#include "src/exceptions/IllegalArgumentException.h"
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namespace storm {
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namespace modelchecker {
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template<typename SparseDtmcModelType>
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SparseDtmcEliminationModelChecker<SparseDtmcModelType>::SparseDtmcEliminationModelChecker(storm::models::sparse::Dtmc<ValueType> const& model) : SparsePropositionalModelChecker<SparseDtmcModelType>(model) {
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// Intentionally left empty.
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}
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template<typename SparseDtmcModelType>
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bool SparseDtmcEliminationModelChecker<SparseDtmcModelType>::canHandle(storm::logic::Formula const& formula) const {
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if (formula.isProbabilityOperatorFormula()) {
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storm::logic::ProbabilityOperatorFormula const& probabilityOperatorFormula = formula.asProbabilityOperatorFormula();
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return this->canHandle(probabilityOperatorFormula.getSubformula());
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} else if (formula.isRewardOperatorFormula()) {
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storm::logic::RewardOperatorFormula const& rewardOperatorFormula = formula.asRewardOperatorFormula();
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return this->canHandle(rewardOperatorFormula.getSubformula());
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} else if (formula.isUntilFormula() || formula.isEventuallyFormula()) {
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if (formula.isUntilFormula()) {
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storm::logic::UntilFormula const& untilFormula = formula.asUntilFormula();
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if (untilFormula.getLeftSubformula().isPropositionalFormula() && untilFormula.getRightSubformula().isPropositionalFormula()) {
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return true;
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}
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} else if (formula.isEventuallyFormula()) {
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storm::logic::EventuallyFormula const& eventuallyFormula = formula.asEventuallyFormula();
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if (eventuallyFormula.getSubformula().isPropositionalFormula()) {
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return true;
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}
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}
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} else if (formula.isReachabilityRewardFormula()) {
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storm::logic::ReachabilityRewardFormula reachabilityRewardFormula = formula.asReachabilityRewardFormula();
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if (reachabilityRewardFormula.getSubformula().isPropositionalFormula()) {
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return true;
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}
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} else if (formula.isConditionalPathFormula()) {
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storm::logic::ConditionalPathFormula conditionalPathFormula = formula.asConditionalPathFormula();
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if (conditionalPathFormula.getLeftSubformula().isEventuallyFormula() && conditionalPathFormula.getRightSubformula().isEventuallyFormula()) {
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return this->canHandle(conditionalPathFormula.getLeftSubformula()) && this->canHandle(conditionalPathFormula.getRightSubformula());
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}
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} else if (formula.isPropositionalFormula()) {
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return true;
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}
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return false;
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}
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template<typename SparseDtmcModelType>
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std::unique_ptr<CheckResult> SparseDtmcEliminationModelChecker<SparseDtmcModelType>::computeUntilProbabilities(storm::logic::UntilFormula const& pathFormula, bool qualitative, boost::optional<OptimizationDirection> const& optimalityType) {
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// Retrieve the appropriate bitvectors by model checking the subformulas.
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std::unique_ptr<CheckResult> leftResultPointer = this->check(pathFormula.getLeftSubformula());
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std::unique_ptr<CheckResult> rightResultPointer = this->check(pathFormula.getRightSubformula());
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storm::storage::BitVector const& phiStates = leftResultPointer->asExplicitQualitativeCheckResult().getTruthValuesVector();
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storm::storage::BitVector const& psiStates = rightResultPointer->asExplicitQualitativeCheckResult().getTruthValuesVector();
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// Do some sanity checks to establish some required properties.
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STORM_LOG_THROW(this->getModel().getInitialStates().getNumberOfSetBits() == 1, storm::exceptions::IllegalArgumentException, "Input model is required to have exactly one initial state.");
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storm::storage::sparse::state_type initialState = *this->getModel().getInitialStates().begin();
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// Then, compute the subset of states that has a probability of 0 or 1, respectively.
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std::pair<storm::storage::BitVector, storm::storage::BitVector> statesWithProbability01 = storm::utility::graph::performProb01(this->getModel(), phiStates, psiStates);
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storm::storage::BitVector statesWithProbability0 = statesWithProbability01.first;
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storm::storage::BitVector statesWithProbability1 = statesWithProbability01.second;
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storm::storage::BitVector maybeStates = ~(statesWithProbability0 | statesWithProbability1);
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// If the initial state is known to have either probability 0 or 1, we can directly return the result.
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if (this->getModel().getInitialStates().isDisjointFrom(maybeStates)) {
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STORM_LOG_DEBUG("The probability of all initial states was found in a preprocessing step.");
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, statesWithProbability0.get(*this->getModel().getInitialStates().begin()) ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>()));
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}
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// Determine the set of states that is reachable from the initial state without jumping over a target state.
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storm::storage::BitVector reachableStates = storm::utility::graph::getReachableStates(this->getModel().getTransitionMatrix(), this->getModel().getInitialStates(), maybeStates, statesWithProbability1);
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// Subtract from the maybe states the set of states that is not reachable (on a path from the initial to a target state).
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maybeStates &= reachableStates;
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// Create a vector for the probabilities to go to a state with probability 1 in one step.
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std::vector<ValueType> oneStepProbabilities = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(maybeStates, statesWithProbability1);
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// Determine the set of initial states of the sub-model.
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storm::storage::BitVector newInitialStates = this->getModel().getInitialStates() % maybeStates;
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// We then build the submatrix that only has the transitions of the maybe states.
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storm::storage::SparseMatrix<ValueType> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(false, maybeStates, maybeStates);
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storm::storage::SparseMatrix<ValueType> submatrixTransposed = submatrix.transpose();
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// Before starting the model checking process, we assign priorities to states so we can use them to
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// impose ordering constraints later.
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std::vector<std::size_t> statePriorities = getStatePriorities(submatrix, submatrixTransposed, newInitialStates, oneStepProbabilities);
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boost::optional<std::vector<ValueType>> missingStateRewards;
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, computeReachabilityValue(submatrix, oneStepProbabilities, submatrixTransposed, newInitialStates, phiStates, psiStates, missingStateRewards, statePriorities)));
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}
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template<typename SparseDtmcModelType>
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std::unique_ptr<CheckResult> SparseDtmcEliminationModelChecker<SparseDtmcModelType>::computeReachabilityRewards(storm::logic::ReachabilityRewardFormula const& rewardPathFormula, boost::optional<std::string> const& rewardModelName, bool qualitative, boost::optional<OptimizationDirection> const& optimalityType) {
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// Retrieve the appropriate bitvectors by model checking the subformulas.
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std::unique_ptr<CheckResult> subResultPointer = this->check(rewardPathFormula.getSubformula());
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storm::storage::BitVector phiStates(this->getModel().getNumberOfStates(), true);
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storm::storage::BitVector const& psiStates = subResultPointer->asExplicitQualitativeCheckResult().getTruthValuesVector();
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// Do some sanity checks to establish some required properties.
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RewardModelType const& rewardModel = this->getModel().getRewardModel(rewardModelName ? rewardModelName.get() : "");
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STORM_LOG_THROW(!rewardModel.empty(), storm::exceptions::IllegalArgumentException, "Input model does not have a reward model.");
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STORM_LOG_THROW(this->getModel().getInitialStates().getNumberOfSetBits() == 1, storm::exceptions::IllegalArgumentException, "Input model is required to have exactly one initial state.");
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storm::storage::sparse::state_type initialState = *this->getModel().getInitialStates().begin();
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// Then, compute the subset of states that has a reachability reward less than infinity.
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storm::storage::BitVector trueStates(this->getModel().getNumberOfStates(), true);
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storm::storage::BitVector infinityStates = storm::utility::graph::performProb1(this->getModel().getBackwardTransitions(), trueStates, psiStates);
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infinityStates.complement();
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storm::storage::BitVector maybeStates = ~psiStates & ~infinityStates;
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// If the initial state is known to have 0 reward or an infinite reward value, we can directly return the result.
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if (infinityStates.get(initialState)) {
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STORM_LOG_DEBUG("The reward of all initial states was found in a preprocessing step.");
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// This is a work around, because not all (e.g. storm::RationalFunction) data types can represent an
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// infinity value.
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<double>(initialState, storm::utility::infinity<double>()));
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}
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if (psiStates.get(initialState)) {
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STORM_LOG_DEBUG("The reward of all initial states was found in a preprocessing step.");
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, storm::utility::zero<ValueType>()));
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}
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// Determine the set of states that is reachable from the initial state without jumping over a target state.
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storm::storage::BitVector reachableStates = storm::utility::graph::getReachableStates(this->getModel().getTransitionMatrix(), this->getModel().getInitialStates(), maybeStates, psiStates);
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// Subtract from the maybe states the set of states that is not reachable (on a path from the initial to a target state).
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maybeStates &= reachableStates;
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// Create a vector for the probabilities to go to a state with probability 1 in one step.
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std::vector<ValueType> oneStepProbabilities = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(maybeStates, psiStates);
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// Determine the set of initial states of the sub-model.
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storm::storage::BitVector newInitialStates = this->getModel().getInitialStates() % maybeStates;
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// We then build the submatrix that only has the transitions of the maybe states.
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storm::storage::SparseMatrix<ValueType> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(false, maybeStates, maybeStates);
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storm::storage::SparseMatrix<ValueType> submatrixTransposed = submatrix.transpose();
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// Before starting the model checking process, we assign priorities to states so we can use them to
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// impose ordering constraints later.
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std::vector<std::size_t> statePriorities = getStatePriorities(submatrix, submatrixTransposed, newInitialStates, oneStepProbabilities);
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// Project the state reward vector to all maybe-states.
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boost::optional<std::vector<ValueType>> optionalStateRewards = rewardModel.getTotalRewardVector(maybeStates.getNumberOfSetBits(), this->getModel().getTransitionMatrix(), maybeStates);
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, computeReachabilityValue(submatrix, oneStepProbabilities, submatrixTransposed, newInitialStates, phiStates, psiStates, optionalStateRewards, statePriorities)));
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}
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template<typename SparseDtmcModelType>
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std::unique_ptr<CheckResult> SparseDtmcEliminationModelChecker<SparseDtmcModelType>::computeConditionalProbabilities(storm::logic::ConditionalPathFormula const& pathFormula, bool qualitative, boost::optional<OptimizationDirection> const& optimalityType) {
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std::chrono::high_resolution_clock::time_point totalTimeStart = std::chrono::high_resolution_clock::now();
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// Retrieve the appropriate bitvectors by model checking the subformulas.
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STORM_LOG_THROW(pathFormula.getLeftSubformula().isEventuallyFormula(), storm::exceptions::InvalidPropertyException, "Expected 'eventually' formula.");
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STORM_LOG_THROW(pathFormula.getRightSubformula().isEventuallyFormula(), storm::exceptions::InvalidPropertyException, "Expected 'eventually' formula.");
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std::unique_ptr<CheckResult> leftResultPointer = this->check(pathFormula.getLeftSubformula().asEventuallyFormula().getSubformula());
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std::unique_ptr<CheckResult> rightResultPointer = this->check(pathFormula.getRightSubformula().asEventuallyFormula().getSubformula());
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storm::storage::BitVector phiStates = leftResultPointer->asExplicitQualitativeCheckResult().getTruthValuesVector();
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storm::storage::BitVector psiStates = rightResultPointer->asExplicitQualitativeCheckResult().getTruthValuesVector();
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storm::storage::BitVector trueStates(this->getModel().getNumberOfStates(), true);
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// Do some sanity checks to establish some required properties.
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// STORM_LOG_WARN_COND(storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationMethod() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationMethod::State, "The chosen elimination method is not available for computing conditional probabilities. Falling back to regular state elimination.");
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STORM_LOG_THROW(this->getModel().getInitialStates().getNumberOfSetBits() == 1, storm::exceptions::IllegalArgumentException, "Input model is required to have exactly one initial state.");
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storm::storage::sparse::state_type initialState = *this->getModel().getInitialStates().begin();
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storm::storage::SparseMatrix<ValueType> backwardTransitions = this->getModel().getBackwardTransitions();
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// Compute the 'true' psi states, i.e. those psi states that can be reached without passing through another psi state first.
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psiStates = storm::utility::graph::getReachableStates(this->getModel().getTransitionMatrix(), this->getModel().getInitialStates(), trueStates, psiStates) & psiStates;
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std::pair<storm::storage::BitVector, storm::storage::BitVector> statesWithProbability01 = storm::utility::graph::performProb01(backwardTransitions, trueStates, psiStates);
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storm::storage::BitVector statesWithProbabilityGreater0 = ~statesWithProbability01.first;
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storm::storage::BitVector statesWithProbability1 = std::move(statesWithProbability01.second);
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STORM_LOG_THROW(this->getModel().getInitialStates().isSubsetOf(statesWithProbabilityGreater0), storm::exceptions::InvalidPropertyException, "The condition of the conditional probability has zero probability.");
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// If the initial state is known to have probability 1 of satisfying the condition, we can apply regular model checking.
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if (this->getModel().getInitialStates().isSubsetOf(statesWithProbability1)) {
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STORM_LOG_INFO("The condition holds with probability 1, so the regular reachability probability is computed.");
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std::shared_ptr<storm::logic::BooleanLiteralFormula> trueFormula = std::make_shared<storm::logic::BooleanLiteralFormula>(true);
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std::shared_ptr<storm::logic::UntilFormula> untilFormula = std::make_shared<storm::logic::UntilFormula>(trueFormula, pathFormula.getLeftSubformula().asSharedPointer());
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return this->computeUntilProbabilities(*untilFormula);
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}
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// From now on, we know the condition does not have a trivial probability in the initial state.
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// Compute the states that can be reached on a path that has a psi state in it.
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storm::storage::BitVector statesWithPsiPredecessor = storm::utility::graph::performProbGreater0(this->getModel().getTransitionMatrix(), trueStates, psiStates);
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storm::storage::BitVector statesReachingPhi = storm::utility::graph::performProbGreater0(backwardTransitions, trueStates, phiStates);
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// The set of states we need to consider are those that have a non-zero probability to satisfy the condition or are on some path that has a psi state in it.
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STORM_LOG_TRACE("Initial state: " << this->getModel().getInitialStates());
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STORM_LOG_TRACE("Phi states: " << phiStates);
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STORM_LOG_TRACE("Psi state: " << psiStates);
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STORM_LOG_TRACE("States with probability greater 0 of satisfying the condition: " << statesWithProbabilityGreater0);
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STORM_LOG_TRACE("States with psi predecessor: " << statesWithPsiPredecessor);
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STORM_LOG_TRACE("States reaching phi: " << statesReachingPhi);
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storm::storage::BitVector maybeStates = statesWithProbabilityGreater0 | (statesWithPsiPredecessor & statesReachingPhi);
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STORM_LOG_TRACE("Found " << maybeStates.getNumberOfSetBits() << " relevant states: " << maybeStates);
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// Determine the set of initial states of the sub-DTMC.
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storm::storage::BitVector newInitialStates = this->getModel().getInitialStates() % maybeStates;
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STORM_LOG_TRACE("Found new initial states: " << newInitialStates << " (old: " << this->getModel().getInitialStates() << ")");
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// Create a dummy vector for the one-step probabilities.
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std::vector<ValueType> oneStepProbabilities(maybeStates.getNumberOfSetBits(), storm::utility::zero<ValueType>());
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// We then build the submatrix that only has the transitions of the maybe states.
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storm::storage::SparseMatrix<ValueType> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(false, maybeStates, maybeStates);
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storm::storage::SparseMatrix<ValueType> submatrixTransposed = submatrix.transpose();
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// The states we want to eliminate are those that are tagged with "maybe" but are not a phi or psi state.
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phiStates = phiStates % maybeStates;
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// If there are no phi states in the reduced model, the conditional probability is trivially zero.
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if (phiStates.empty()) {
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return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, storm::utility::zero<ValueType>()));
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}
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psiStates = psiStates % maybeStates;
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// Keep only the states that we do not eliminate in the maybe states.
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maybeStates = phiStates | psiStates;
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STORM_LOG_TRACE("Phi states in reduced model " << phiStates);
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STORM_LOG_TRACE("Psi states in reduced model " << psiStates);
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storm::storage::BitVector statesToEliminate = ~maybeStates & ~newInitialStates;
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STORM_LOG_TRACE("Eliminating the states " << statesToEliminate);
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// Before starting the model checking process, we assign priorities to states so we can use them to
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// impose ordering constraints later.
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std::vector<std::size_t> statePriorities = getStatePriorities(submatrix, submatrixTransposed, newInitialStates, oneStepProbabilities);
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std::vector<storm::storage::sparse::state_type> states(statesToEliminate.begin(), statesToEliminate.end());
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// Sort the states according to the priorities.
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std::sort(states.begin(), states.end(), [&statePriorities] (storm::storage::sparse::state_type const& a, storm::storage::sparse::state_type const& b) { return statePriorities[a] < statePriorities[b]; });
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STORM_LOG_INFO("Computing conditional probilities." << std::endl);
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STORM_LOG_INFO("Eliminating " << states.size() << " states using the state elimination technique." << std::endl);
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boost::optional<std::vector<ValueType>> missingStateRewards;
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std::chrono::high_resolution_clock::time_point conversionStart = std::chrono::high_resolution_clock::now();
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storm::storage::FlexibleSparseMatrix<ValueType> flexibleMatrix(submatrix);
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storm::storage::FlexibleSparseMatrix<ValueType> flexibleBackwardTransitions(submatrixTransposed, true);
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std::chrono::high_resolution_clock::time_point conversionEnd = std::chrono::high_resolution_clock::now();
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std::chrono::high_resolution_clock::time_point modelCheckingStart = std::chrono::high_resolution_clock::now();
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for (auto const& state : states) {
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storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, state, flexibleBackwardTransitions, missingStateRewards);
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}
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STORM_LOG_INFO("Eliminated " << states.size() << " states." << std::endl);
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// Eliminate the transitions going into the initial state (if there are any).
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if (!flexibleBackwardTransitions.getRow(*newInitialStates.begin()).empty()) {
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storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, *newInitialStates.begin(), flexibleBackwardTransitions, missingStateRewards, false);
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}
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// Now we need to basically eliminate all chains of not-psi states after phi states and chains of not-phi
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// states after psi states.
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for (auto const& trans1 : flexibleMatrix.getRow(*newInitialStates.begin())) {
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auto initialStateSuccessor = trans1.getColumn();
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STORM_LOG_TRACE("Exploring successor " << initialStateSuccessor << " of the initial state.");
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if (phiStates.get(initialStateSuccessor)) {
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STORM_LOG_TRACE("Is a phi state.");
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// If the state is both a phi and a psi state, we do not need to eliminate chains.
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if (psiStates.get(initialStateSuccessor)) {
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continue;
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}
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// At this point, we know that the state satisfies phi and not psi.
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// This means, we must compute the probability to reach psi states, which in turn means that we need
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// to eliminate all chains of non-psi states between the current state and psi states.
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bool hasNonPsiSuccessor = true;
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while (hasNonPsiSuccessor) {
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hasNonPsiSuccessor = false;
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// Only treat the state if it has an outgoing transition other than a self-loop.
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auto const currentRow = flexibleMatrix.getRow(initialStateSuccessor);
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if (currentRow.size() > 1 || (!currentRow.empty() && currentRow.front().getColumn() != initialStateSuccessor)) {
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for (auto const& element : currentRow) {
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// If any of the successors is a phi state, we eliminate it (wrt. all its phi predecessors).
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if (!psiStates.get(element.getColumn())) {
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typename storm::storage::FlexibleSparseMatrix<ValueType>::row_type const& successorRow = flexibleMatrix.getRow(element.getColumn());
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// Eliminate the successor only if there possibly is a psi state reachable through it.
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if (successorRow.size() > 1 || (!successorRow.empty() && successorRow.front().getColumn() != element.getColumn())) {
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STORM_LOG_TRACE("Found non-psi successor " << element.getColumn() << " that needs to be eliminated.");
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storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, element.getColumn(), flexibleBackwardTransitions, missingStateRewards, false, true, phiStates);
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hasNonPsiSuccessor = true;
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}
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}
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}
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STORM_LOG_ASSERT(!flexibleMatrix.getRow(initialStateSuccessor).empty(), "(1) New transitions expected to be non-empty.");
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}
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}
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} else {
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STORM_LOG_ASSERT(psiStates.get(initialStateSuccessor), "Expected psi state.");
|
|
STORM_LOG_TRACE("Is a psi state.");
|
|
|
|
// At this point, we know that the state satisfies psi and not phi.
|
|
// This means, we must compute the probability to reach phi states, which in turn means that we need
|
|
// to eliminate all chains of non-phi states between the current state and phi states.
|
|
|
|
bool hasNonPhiSuccessor = true;
|
|
while (hasNonPhiSuccessor) {
|
|
hasNonPhiSuccessor = false;
|
|
|
|
// Only treat the state if it has an outgoing transition other than a self-loop.
|
|
auto const currentRow = flexibleMatrix.getRow(initialStateSuccessor);
|
|
if (currentRow.size() > 1 || (!currentRow.empty() && currentRow.front().getColumn() != initialStateSuccessor)) {
|
|
for (auto const& element : currentRow) {
|
|
// If any of the successors is a psi state, we eliminate it (wrt. all its psi predecessors).
|
|
if (!phiStates.get(element.getColumn())) {
|
|
typename storm::storage::FlexibleSparseMatrix<ValueType>::row_type const& successorRow = flexibleMatrix.getRow(element.getColumn());
|
|
if (successorRow.size() > 1 || (!successorRow.empty() && successorRow.front().getColumn() != element.getColumn())) {
|
|
STORM_LOG_TRACE("Found non-phi successor " << element.getColumn() << " that needs to be eliminated.");
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, element.getColumn(), flexibleBackwardTransitions, missingStateRewards, false, true, psiStates);
|
|
hasNonPhiSuccessor = true;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
ValueType numerator = storm::utility::zero<ValueType>();
|
|
ValueType denominator = storm::utility::zero<ValueType>();
|
|
|
|
for (auto const& trans1 : flexibleMatrix.getRow(*newInitialStates.begin())) {
|
|
auto initialStateSuccessor = trans1.getColumn();
|
|
if (phiStates.get(initialStateSuccessor)) {
|
|
if (psiStates.get(initialStateSuccessor)) {
|
|
numerator += trans1.getValue();
|
|
denominator += trans1.getValue();
|
|
} else {
|
|
ValueType additiveTerm = storm::utility::zero<ValueType>();
|
|
for (auto const& trans2 : flexibleMatrix.getRow(initialStateSuccessor)) {
|
|
if (psiStates.get(trans2.getColumn())) {
|
|
additiveTerm += trans2.getValue();
|
|
}
|
|
}
|
|
additiveTerm *= trans1.getValue();
|
|
numerator += additiveTerm;
|
|
denominator += additiveTerm;
|
|
}
|
|
} else {
|
|
STORM_LOG_ASSERT(psiStates.get(initialStateSuccessor), "Expected psi state.");
|
|
denominator += trans1.getValue();
|
|
ValueType additiveTerm = storm::utility::zero<ValueType>();
|
|
for (auto const& trans2 : flexibleMatrix.getRow(initialStateSuccessor)) {
|
|
if (phiStates.get(trans2.getColumn())) {
|
|
additiveTerm += trans2.getValue();
|
|
}
|
|
}
|
|
numerator += trans1.getValue() * additiveTerm;
|
|
}
|
|
}
|
|
std::chrono::high_resolution_clock::time_point modelCheckingEnd = std::chrono::high_resolution_clock::now();
|
|
std::chrono::high_resolution_clock::time_point totalTimeEnd = std::chrono::high_resolution_clock::now();
|
|
|
|
if (storm::settings::generalSettings().isShowStatisticsSet()) {
|
|
std::chrono::high_resolution_clock::duration conversionTime = conversionEnd - conversionStart;
|
|
std::chrono::milliseconds conversionTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(conversionTime);
|
|
std::chrono::high_resolution_clock::duration modelCheckingTime = modelCheckingEnd - modelCheckingStart;
|
|
std::chrono::milliseconds modelCheckingTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(modelCheckingTime);
|
|
std::chrono::high_resolution_clock::duration totalTime = totalTimeEnd - totalTimeStart;
|
|
std::chrono::milliseconds totalTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(totalTime);
|
|
|
|
STORM_PRINT_AND_LOG(std::endl);
|
|
STORM_PRINT_AND_LOG("Time breakdown:" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * time for conversion: " << conversionTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * time for checking: " << modelCheckingTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG("------------------------------------------" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * total time: " << totalTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG(std::endl);
|
|
}
|
|
|
|
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, numerator / denominator));
|
|
}
|
|
|
|
template<typename SparseDtmcModelType>
|
|
typename SparseDtmcEliminationModelChecker<SparseDtmcModelType>::ValueType SparseDtmcEliminationModelChecker<SparseDtmcModelType>::computeReachabilityValue(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType>& oneStepProbabilities, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& initialStates, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, boost::optional<std::vector<ValueType>>& stateRewards, boost::optional<std::vector<std::size_t>> const& statePriorities) {
|
|
std::chrono::high_resolution_clock::time_point totalTimeStart = std::chrono::high_resolution_clock::now();
|
|
|
|
// Create a bit vector that represents the subsystem of states we still have to eliminate.
|
|
storm::storage::BitVector subsystem = storm::storage::BitVector(transitionMatrix.getRowCount(), true);
|
|
|
|
std::chrono::high_resolution_clock::time_point conversionStart = std::chrono::high_resolution_clock::now();
|
|
// Then, we convert the reduced matrix to a more flexible format to be able to perform state elimination more easily.
|
|
storm::storage::FlexibleSparseMatrix<ValueType> flexibleMatrix(transitionMatrix);
|
|
storm::storage::FlexibleSparseMatrix<ValueType> flexibleBackwardTransitions(backwardTransitions, true);
|
|
auto conversionEnd = std::chrono::high_resolution_clock::now();
|
|
|
|
std::chrono::high_resolution_clock::time_point modelCheckingStart = std::chrono::high_resolution_clock::now();
|
|
uint_fast64_t maximalDepth = 0;
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationMethod() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationMethod::State) {
|
|
// If we are required to do pure state elimination, we simply create a vector of all states to
|
|
// eliminate and sort it according to the given priorities.
|
|
|
|
// Remove the initial state from the states which we need to eliminate.
|
|
subsystem &= ~initialStates;
|
|
std::vector<storm::storage::sparse::state_type> states(subsystem.begin(), subsystem.end());
|
|
|
|
if (statePriorities) {
|
|
std::sort(states.begin(), states.end(), [&statePriorities] (storm::storage::sparse::state_type const& a, storm::storage::sparse::state_type const& b) { return statePriorities.get()[a] < statePriorities.get()[b]; });
|
|
}
|
|
|
|
STORM_LOG_DEBUG("Eliminating " << states.size() << " states using the state elimination technique." << std::endl);
|
|
for (auto const& state : states) {
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, state, flexibleBackwardTransitions, stateRewards);
|
|
}
|
|
STORM_LOG_DEBUG("Eliminated " << states.size() << " states." << std::endl);
|
|
} else if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationMethod() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationMethod::Hybrid) {
|
|
// When using the hybrid technique, we recursively treat the SCCs up to some size.
|
|
std::vector<storm::storage::sparse::state_type> entryStateQueue;
|
|
STORM_LOG_DEBUG("Eliminating " << subsystem.size() << " states using the hybrid elimination technique." << std::endl);
|
|
maximalDepth = treatScc(flexibleMatrix, oneStepProbabilities, initialStates, subsystem, transitionMatrix, flexibleBackwardTransitions, false, 0, storm::settings::sparseDtmcEliminationModelCheckerSettings().getMaximalSccSize(), entryStateQueue, stateRewards, statePriorities);
|
|
|
|
// If the entry states were to be eliminated last, we need to do so now.
|
|
STORM_LOG_DEBUG("Eliminating " << entryStateQueue.size() << " entry states as a last step.");
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().isEliminateEntryStatesLastSet()) {
|
|
for (auto const& state : entryStateQueue) {
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, state, flexibleBackwardTransitions, stateRewards);
|
|
}
|
|
}
|
|
STORM_LOG_DEBUG("Eliminated " << subsystem.size() << " states." << std::endl);
|
|
}
|
|
|
|
// Finally eliminate initial state.
|
|
if (!stateRewards) {
|
|
// If we are computing probabilities, then we can simply call the state elimination procedure. It
|
|
// will scale the transition row of the initial state with 1/(1-loopProbability).
|
|
STORM_LOG_INFO("Eliminating initial state " << *initialStates.begin() << "." << std::endl);
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(flexibleMatrix, oneStepProbabilities, *initialStates.begin(), flexibleBackwardTransitions, stateRewards);
|
|
} else {
|
|
// If we are computing rewards, we cannot call the state elimination procedure for technical reasons.
|
|
// Instead, we need to get rid of a potential loop in this state explicitly.
|
|
|
|
// Start by finding the self-loop element. Since it can only be the only remaining outgoing transition
|
|
// of the initial state, this amounts to checking whether the outgoing transitions of the initial
|
|
// state are non-empty.
|
|
if (!flexibleMatrix.getRow(*initialStates.begin()).empty()) {
|
|
STORM_LOG_ASSERT(flexibleMatrix.getRow(*initialStates.begin()).size() == 1, "At most one outgoing transition expected at this point, but found more.");
|
|
STORM_LOG_ASSERT(flexibleMatrix.getRow(*initialStates.begin()).front().getColumn() == *initialStates.begin(), "Remaining entry should be a self-loop, but it is not.");
|
|
ValueType loopProbability = flexibleMatrix.getRow(*initialStates.begin()).front().getValue();
|
|
loopProbability = storm::utility::one<ValueType>() / (storm::utility::one<ValueType>() - loopProbability);
|
|
STORM_LOG_DEBUG("Scaling the reward of the initial state " << stateRewards.get()[(*initialStates.begin())] << " with " << loopProbability);
|
|
stateRewards.get()[(*initialStates.begin())] *= loopProbability;
|
|
flexibleMatrix.getRow(*initialStates.begin()).clear();
|
|
}
|
|
}
|
|
|
|
// Make sure that we have eliminated all transitions from the initial state.
|
|
STORM_LOG_ASSERT(flexibleMatrix.getRow(*initialStates.begin()).empty(), "The transitions of the initial states are non-empty.");
|
|
|
|
std::chrono::high_resolution_clock::time_point modelCheckingEnd = std::chrono::high_resolution_clock::now();
|
|
std::chrono::high_resolution_clock::time_point totalTimeEnd = std::chrono::high_resolution_clock::now();
|
|
|
|
if (storm::settings::generalSettings().isShowStatisticsSet()) {
|
|
std::chrono::high_resolution_clock::duration conversionTime = conversionEnd - conversionStart;
|
|
std::chrono::milliseconds conversionTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(conversionTime);
|
|
std::chrono::high_resolution_clock::duration modelCheckingTime = modelCheckingEnd - modelCheckingStart;
|
|
std::chrono::milliseconds modelCheckingTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(modelCheckingTime);
|
|
std::chrono::high_resolution_clock::duration totalTime = totalTimeEnd - totalTimeStart;
|
|
std::chrono::milliseconds totalTimeInMilliseconds = std::chrono::duration_cast<std::chrono::milliseconds>(totalTime);
|
|
|
|
STORM_PRINT_AND_LOG(std::endl);
|
|
STORM_PRINT_AND_LOG("Time breakdown:" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * time for conversion: " << conversionTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * time for checking: " << modelCheckingTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG("------------------------------------------" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * total time: " << totalTimeInMilliseconds.count() << "ms" << std::endl);
|
|
STORM_PRINT_AND_LOG(std::endl);
|
|
STORM_PRINT_AND_LOG("Other:" << std::endl);
|
|
STORM_PRINT_AND_LOG(" * number of states eliminated: " << transitionMatrix.getRowCount() << std::endl);
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationMethod() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationMethod::Hybrid) {
|
|
STORM_PRINT_AND_LOG(" * maximal depth of SCC decomposition: " << maximalDepth << std::endl);
|
|
}
|
|
}
|
|
|
|
// Now, we return the value for the only initial state.
|
|
STORM_LOG_DEBUG("Simplifying and returning result.");
|
|
if (stateRewards) {
|
|
return storm::utility::simplify(stateRewards.get()[*initialStates.begin()]);
|
|
} else {
|
|
return oneStepProbabilities[*initialStates.begin()];
|
|
}
|
|
}
|
|
|
|
template<typename SparseDtmcModelType>
|
|
std::vector<std::size_t> SparseDtmcEliminationModelChecker<SparseDtmcModelType>::getStatePriorities(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& transitionMatrixTransposed, storm::storage::BitVector const& initialStates, std::vector<ValueType> const& oneStepProbabilities) {
|
|
std::vector<std::size_t> statePriorities(transitionMatrix.getRowCount());
|
|
std::vector<std::size_t> states(transitionMatrix.getRowCount());
|
|
for (std::size_t index = 0; index < states.size(); ++index) {
|
|
states[index] = index;
|
|
}
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::Random) {
|
|
std::random_shuffle(states.begin(), states.end());
|
|
} else {
|
|
std::vector<std::size_t> distances;
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::Forward || storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::ForwardReversed) {
|
|
distances = storm::utility::graph::getDistances(transitionMatrix, initialStates);
|
|
} else if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::Backward || storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::BackwardReversed) {
|
|
// Since the target states were eliminated from the matrix already, we construct a replacement by
|
|
// treating all states that have some non-zero probability to go to a target state in one step.
|
|
storm::storage::BitVector pseudoTargetStates(transitionMatrix.getRowCount());
|
|
for (std::size_t index = 0; index < oneStepProbabilities.size(); ++index) {
|
|
if (oneStepProbabilities[index] != storm::utility::zero<ValueType>()) {
|
|
pseudoTargetStates.set(index);
|
|
}
|
|
}
|
|
|
|
distances = storm::utility::graph::getDistances(transitionMatrixTransposed, pseudoTargetStates);
|
|
} else {
|
|
STORM_LOG_ASSERT(false, "Illegal sorting order selected.");
|
|
}
|
|
|
|
// In case of the forward or backward ordering, we can sort the states according to the distances.
|
|
if (storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::Forward || storm::settings::sparseDtmcEliminationModelCheckerSettings().getEliminationOrder() == storm::settings::modules::SparseDtmcEliminationModelCheckerSettings::EliminationOrder::Backward) {
|
|
std::sort(states.begin(), states.end(), [&distances] (storm::storage::sparse::state_type const& state1, storm::storage::sparse::state_type const& state2) { return distances[state1] < distances[state2]; } );
|
|
} else {
|
|
// Otherwise, we sort them according to descending distances.
|
|
std::sort(states.begin(), states.end(), [&distances] (storm::storage::sparse::state_type const& state1, storm::storage::sparse::state_type const& state2) { return distances[state1] > distances[state2]; } );
|
|
}
|
|
}
|
|
|
|
// Now convert the ordering of the states to priorities.
|
|
for (std::size_t index = 0; index < states.size(); ++index) {
|
|
statePriorities[states[index]] = index;
|
|
}
|
|
|
|
return statePriorities;
|
|
}
|
|
|
|
template<typename SparseDtmcModelType>
|
|
uint_fast64_t SparseDtmcEliminationModelChecker<SparseDtmcModelType>::treatScc(storm::storage::FlexibleSparseMatrix<ValueType>& matrix, std::vector<ValueType>& oneStepProbabilities, storm::storage::BitVector const& entryStates, storm::storage::BitVector const& scc, storm::storage::SparseMatrix<ValueType> const& forwardTransitions, storm::storage::FlexibleSparseMatrix<ValueType>& backwardTransitions, bool eliminateEntryStates, uint_fast64_t level, uint_fast64_t maximalSccSize, std::vector<storm::storage::sparse::state_type>& entryStateQueue, boost::optional<std::vector<ValueType>>& stateRewards, boost::optional<std::vector<std::size_t>> const& statePriorities) {
|
|
uint_fast64_t maximalDepth = level;
|
|
|
|
// If the SCCs are large enough, we try to split them further.
|
|
if (scc.getNumberOfSetBits() > maximalSccSize) {
|
|
STORM_LOG_TRACE("SCC is large enough (" << scc.getNumberOfSetBits() << " states) to be decomposed further.");
|
|
|
|
// Here, we further decompose the SCC into sub-SCCs.
|
|
storm::storage::StronglyConnectedComponentDecomposition<ValueType> decomposition(forwardTransitions, scc & ~entryStates, false, false);
|
|
STORM_LOG_TRACE("Decomposed SCC into " << decomposition.size() << " sub-SCCs.");
|
|
|
|
// Store a bit vector of remaining SCCs so we can be flexible when it comes to the order in which
|
|
// we eliminate the SCCs.
|
|
storm::storage::BitVector remainingSccs(decomposition.size(), true);
|
|
|
|
// First, get rid of the trivial SCCs.
|
|
std::vector<std::pair<storm::storage::sparse::state_type, uint_fast64_t>> trivialSccs;
|
|
for (uint_fast64_t sccIndex = 0; sccIndex < decomposition.size(); ++sccIndex) {
|
|
storm::storage::StronglyConnectedComponent const& scc = decomposition.getBlock(sccIndex);
|
|
if (scc.isTrivial()) {
|
|
storm::storage::sparse::state_type onlyState = *scc.begin();
|
|
trivialSccs.emplace_back(onlyState, sccIndex);
|
|
}
|
|
}
|
|
|
|
// If we are given priorities, sort the trivial SCCs accordingly.
|
|
if (statePriorities) {
|
|
std::sort(trivialSccs.begin(), trivialSccs.end(), [&statePriorities] (std::pair<storm::storage::sparse::state_type, uint_fast64_t> const& a, std::pair<storm::storage::sparse::state_type, uint_fast64_t> const& b) { return statePriorities.get()[a.first] < statePriorities.get()[b.first]; });
|
|
}
|
|
|
|
STORM_LOG_TRACE("Eliminating " << trivialSccs.size() << " trivial SCCs.");
|
|
for (auto const& stateIndexPair : trivialSccs) {
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(matrix, oneStepProbabilities, stateIndexPair.first, backwardTransitions, stateRewards);
|
|
remainingSccs.set(stateIndexPair.second, false);
|
|
}
|
|
STORM_LOG_TRACE("Eliminated all trivial SCCs.");
|
|
|
|
// And then recursively treat the remaining sub-SCCs.
|
|
STORM_LOG_TRACE("Eliminating " << remainingSccs.getNumberOfSetBits() << " remaining SCCs on level " << level << ".");
|
|
for (auto sccIndex : remainingSccs) {
|
|
storm::storage::StronglyConnectedComponent const& newScc = decomposition.getBlock(sccIndex);
|
|
|
|
// Rewrite SCC into bit vector and subtract it from the remaining states.
|
|
storm::storage::BitVector newSccAsBitVector(forwardTransitions.getRowCount(), newScc.begin(), newScc.end());
|
|
|
|
// Determine the set of entry states of the SCC.
|
|
storm::storage::BitVector entryStates(forwardTransitions.getRowCount());
|
|
for (auto const& state : newScc) {
|
|
for (auto const& predecessor : backwardTransitions.getRow(state)) {
|
|
if (predecessor.getValue() != storm::utility::zero<ValueType>() && !newSccAsBitVector.get(predecessor.getColumn())) {
|
|
entryStates.set(state);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Recursively descend in SCC-hierarchy.
|
|
uint_fast64_t depth = treatScc(matrix, oneStepProbabilities, entryStates, newSccAsBitVector, forwardTransitions, backwardTransitions, !storm::settings::sparseDtmcEliminationModelCheckerSettings().isEliminateEntryStatesLastSet(), level + 1, maximalSccSize, entryStateQueue, stateRewards, statePriorities);
|
|
maximalDepth = std::max(maximalDepth, depth);
|
|
}
|
|
|
|
} else {
|
|
// In this case, we perform simple state elimination in the current SCC.
|
|
STORM_LOG_TRACE("SCC of size " << scc.getNumberOfSetBits() << " is small enough to be eliminated directly.");
|
|
storm::storage::BitVector remainingStates = scc & ~entryStates;
|
|
|
|
std::vector<uint_fast64_t> states(remainingStates.begin(), remainingStates.end());
|
|
|
|
// If we are given priorities, sort the trivial SCCs accordingly.
|
|
if (statePriorities) {
|
|
std::sort(states.begin(), states.end(), [&statePriorities] (storm::storage::sparse::state_type const& a, storm::storage::sparse::state_type const& b) { return statePriorities.get()[a] < statePriorities.get()[b]; });
|
|
}
|
|
|
|
// Eliminate the remaining states that do not have a self-loop (in the current, i.e. modified)
|
|
// transition probability matrix.
|
|
for (auto const& state : states) {
|
|
storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(matrix, oneStepProbabilities, state, backwardTransitions, stateRewards);
|
|
}
|
|
|
|
STORM_LOG_TRACE("Eliminated all states of SCC.");
|
|
}
|
|
|
|
// Finally, eliminate the entry states (if we are required to do so).
|
|
if (eliminateEntryStates) {
|
|
STORM_LOG_TRACE("Finally, eliminating/adding entry states.");
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for (auto state : entryStates) {
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storm::storage::FlexibleSparseMatrix<ValueType>::eliminateState(matrix, oneStepProbabilities, state, backwardTransitions, stateRewards);
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}
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STORM_LOG_TRACE("Eliminated/added entry states.");
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} else {
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for (auto state : entryStates) {
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entryStateQueue.push_back(state);
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}
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}
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|
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return maximalDepth;
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}
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template class SparseDtmcEliminationModelChecker<storm::models::sparse::Dtmc<double>>;
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#ifdef STORM_HAVE_CARL
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template class SparseDtmcEliminationModelChecker<storm::models::sparse::Dtmc<storm::RationalFunction>>;
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#endif
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} // namespace modelchecker
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} // namespace storm
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