TimQu
10 years ago
15 changed files with 590 additions and 17 deletions
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5src/CMakeLists.txt
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3src/logic/FragmentChecker.cpp
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18src/logic/FragmentSpecification.cpp
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4src/logic/FragmentSpecification.h
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25src/logic/MultiObjectiveFormula.cpp
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10src/logic/MultiObjectiveFormula.h
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7src/modelchecker/AbstractModelChecker.cpp
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6src/modelchecker/AbstractModelChecker.h
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46src/modelchecker/multiobjective/SparseMdpMultiObjectiveModelChecker.cpp
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27src/modelchecker/multiobjective/SparseMdpMultiObjectiveModelChecker.h
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254src/modelchecker/multiobjective/helper/SparseMdpMultiObjectivePreprocessingHelper.cpp
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62src/modelchecker/multiobjective/helper/SparseMdpMultiObjectivePreprocessingHelper.h
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83src/modelchecker/multiobjective/helper/SparseMultiObjectiveModelCheckerInformation.h
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17src/utility/storm.h
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40test/functional/logic/FragmentCheckerTest.cpp
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#include "src/modelchecker/multiobjective/SparseMdpMultiObjectiveModelChecker.h"
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#include "src/utility/macros.h"
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#include "src/logic/Formulas.h"
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#include "src/logic/FragmentSpecification.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/modelchecker/multiobjective/helper/SparseMdpMultiObjectivePreprocessingHelper.h"
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#include "src/modelchecker/multiobjective/helper/SparseMultiObjectiveModelCheckerInformation.h"
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#include "src/exceptions/NotImplementedException.h"
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namespace storm { |
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namespace modelchecker { |
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template<typename SparseMdpModelType> |
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SparseMdpMultiObjectiveModelChecker<SparseMdpModelType>::SparseMdpMultiObjectiveModelChecker(SparseMdpModelType const& model) : SparseMdpPrctlModelChecker<SparseMdpModelType>(model) { |
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// Intentionally left empty.
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectiveModelChecker<SparseMdpModelType>::canHandle(CheckTask<storm::logic::Formula> const& checkTask) const { |
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// A formula without multi objective (sub)formulas can be handled by the base class
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if(SparseMdpPrctlModelChecker<SparseMdpModelType>::canHandle(checkTask)) return true; |
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//In general, each initial state requires an individual scheduler (in contrast to single objective model checking). Let's exclude this.
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if(this->getModel().getInitialStates().getNumberOfSetBits() > 1) return false; |
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if(!checkTask.isOnlyInitialStatesRelevantSet()) return false; |
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return checkTask.getFormula().isInFragment(storm::logic::multiObjective()); |
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} |
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template<typename SparseMdpModelType> |
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std::unique_ptr<CheckResult> SparseMdpMultiObjectiveModelChecker<SparseMdpModelType>::checkMultiObjectiveFormula(CheckTask<storm::logic::MultiObjectiveFormula> const& checkTask) { |
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helper::SparseMultiObjectiveModelCheckerInformation<SparseMdpModelType> info = helper::SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(checkTask.getFormula(), this->getModel()); |
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return std::unique_ptr<CheckResult>(new ExplicitQualitativeCheckResult()); |
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} |
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template class SparseMdpMultiObjectiveModelChecker<storm::models::sparse::Mdp<double>>; |
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} |
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} |
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#ifndef STORM_MODELCHECKER_MULTIOBJECTIVE_SPARSEMDPMULTIOBJECTIVEMODELCHECKER_H_ |
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#define STORM_MODELCHECKER_MULTIOBJECTIVE_SPARSEMDPMULTIOBJECTIVEMODELCHECKER_H_ |
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#include "src/modelchecker/prctl/SparseMdpPrctlModelChecker.h" |
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namespace storm { |
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namespace modelchecker { |
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template<class SparseMdpModelType> |
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class SparseMdpMultiObjectiveModelChecker : public SparseMdpPrctlModelChecker<SparseMdpModelType> { |
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public: |
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typedef typename SparseMdpModelType::ValueType ValueType; |
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typedef typename SparseMdpModelType::RewardModelType RewardModelType; |
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explicit SparseMdpMultiObjectiveModelChecker(SparseMdpModelType const& model); |
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virtual bool canHandle(CheckTask<storm::logic::Formula> const& checkTask) const override; |
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virtual std::unique_ptr<CheckResult> checkMultiObjectiveFormula(CheckTask<storm::logic::MultiObjectiveFormula> const& checkTask) override; |
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private: |
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}; |
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} // namespace modelchecker |
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} // namespace storm |
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#endif /* STORM_MODELCHECKER_MULTIOBJECTIVE_SPARSEMDPMULTIOBJECTIVEMODELCHECKER_H_ */ |
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#include "src/modelchecker/multiobjective/helper/SparseMdpMultiObjectivePreprocessingHelper.h"
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#include "src/models/sparse/Mdp.h"
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#include "src/models/sparse/StandardRewardModel.h"
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#include "src/modelchecker/propositional/SparsePropositionalModelChecker.h"
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#include "src/modelchecker/results/ExplicitQualitativeCheckResult.h"
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#include "src/storage/BitVector.h"
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#include "src/utility/macros.h"
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#include "src/utility/vector.h"
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#include "src/utility/graph.h"
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#include "src/exceptions/InvalidPropertyException.h"
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#include "src/exceptions/UnexpectedException.h"
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namespace storm { |
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namespace modelchecker { |
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namespace helper { |
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template<typename SparseMdpModelType> |
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SparseMultiObjectiveModelCheckerInformation<SparseMdpModelType> SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::MultiObjectiveFormula const& originalFormula, SparseMdpModelType originalModel) { |
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Information info(std::move(originalModel)); |
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//Initialize the state mapping.
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info.getNewToOldStateMapping().reserve(info.getModel().getNumberOfStates()); |
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for(uint_fast64_t state = 0; state < info.getModel().getNumberOfStates(); ++state){ |
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info.getNewToOldStateMapping().push_back(state); |
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} |
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//Gather information regarding the individual objectives
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if(!gatherObjectiveInformation(originalFormula, info)){ |
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STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not gather information for objectives " << originalFormula << "."); |
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} |
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// Find out whether negative rewards should be considered.
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if(!setWhetherNegativeRewardsAreConsidered(info)){ |
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STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not find out whether to consider negative rewards " << originalFormula << "."); |
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} |
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//Invoke preprocessing on objectives
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bool success = false; |
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for (auto& obj : info.getObjectives()){ |
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STORM_LOG_DEBUG("Preprocessing objective " << *obj.originalFormula << "."); |
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if(obj.originalFormula->isProbabilityOperatorFormula()){ |
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success = preprocess(obj.originalFormula->asProbabilityOperatorFormula(), info, obj); |
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} else if(obj.originalFormula->isRewardOperatorFormula()){ |
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success = preprocess(obj.originalFormula->asRewardOperatorFormula(), info, obj); |
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} |
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} |
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STORM_LOG_THROW(success, storm::exceptions::InvalidPropertyException, "Could not preprocess for the formula " << originalFormula << "."); |
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return info; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::gatherObjectiveInformation(storm::logic::MultiObjectiveFormula const& formula, Information& info) { |
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for(auto const& subF : formula.getSubFormulas()){ |
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if(!subF->isOperatorFormula()){ |
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STORM_LOG_ERROR("Expected an OperatorFormula as subformula of " << formula << " but got " << *subF); |
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return false; |
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} |
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storm::logic::OperatorFormula const& f = subF->asOperatorFormula(); |
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typename Information::ObjectiveInformation objective; |
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objective.originalFormula = subF; |
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if(f.hasBound()){ |
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objective.threshold = f.getBound().threshold; |
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// Note that we minimize if the comparison type is an upper bound since we are interested in the EXISTENCE of a scheduler...
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objective.originalFormulaMinimizes = !storm::logic::isLowerBound(f.getBound().comparisonType); |
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} else if (f.hasOptimalityType()){ |
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objective.originalFormulaMinimizes = storm::solver::minimize(f.getOptimalityType()); |
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} else { |
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STORM_LOG_ERROR("Current objective" << f << "does not specify whether to minimize or maximize"); |
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} |
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objective.rewardModelName = "objective" + std::to_string(info.getObjectives().size()); |
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// Make sure the new reward model gets a unique name
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while(info.getModel().hasRewardModel(objective.rewardModelName)){ |
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objective.rewardModelName = "_" + objective.rewardModelName; |
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} |
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if(!setStepBoundOfObjective(objective)) { |
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return false; |
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} |
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info.getObjectives().push_back(std::move(objective)); |
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} |
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return true; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::setStepBoundOfObjective(typename Information::ObjectiveInformation& objective){ |
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if(objective.originalFormula->isProbabilityOperatorFormula()){ |
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storm::logic::Formula const& f = objective.originalFormula->asProbabilityOperatorFormula().getSubformula(); |
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if(f.isBoundedUntilFormula()){ |
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if(f.asBoundedUntilFormula().hasDiscreteTimeBound()){ |
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objective.stepBound = f.asBoundedUntilFormula().getDiscreteTimeBound(); |
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} else { |
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STORM_LOG_ERROR("Expected a discrete time bound at boundedUntilFormula but got" << f << "."); |
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return false; |
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} |
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} |
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} else if(objective.originalFormula->isRewardOperatorFormula()){ |
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storm::logic::Formula const& f = objective.originalFormula->asRewardOperatorFormula(); |
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if(f.isCumulativeRewardFormula()){ |
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if(f.asCumulativeRewardFormula().hasDiscreteTimeBound()){ |
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objective.stepBound = f.asCumulativeRewardFormula().getDiscreteTimeBound(); |
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} else { |
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STORM_LOG_ERROR("Expected a discrete time bound at cumulativeRewardFormula but got" << f << "."); |
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return false; |
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} |
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} |
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} else { |
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STORM_LOG_ERROR("Expected a Probability or Reward OperatorFormula but got " << *objective.originalFormula << "."); |
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return false; |
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} |
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return true; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::setWhetherNegativeRewardsAreConsidered(Information& info) { |
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// Negative Rewards are considered whenever there is an unbounded reward objective that requires to minimize.
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// If there is no unbounded reward objective, we make a majority vote.
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uint_fast64_t numOfMinimizingObjectives = 0; |
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for(auto const& obj : info.getObjectives()){ |
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if(obj.originalFormula->isRewardOperatorFormula() && !obj.stepBound){ |
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info.setNegativeRewardsConsidered(obj.originalFormulaMinimizes); |
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return true; |
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} |
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numOfMinimizingObjectives += obj.originalFormulaMinimizes ? 1 : 0; |
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} |
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// Reaching this point means that we make a majority vote
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info.setNegativeRewardsConsidered(numOfMinimizingObjectives*2 > info.getObjectives().size()); |
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return true; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::ProbabilityOperatorFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective) { |
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// Check if we need to complement the property, e.g., P<0.3 [ F "a" ] ---> P >=0.7 [ G !"a" ]
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// This is the case if the formula requires to minimize and positive rewards are considered or vice versa
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if(info.areNegativeRewardsConsidered() != currentObjective.originalFormulaMinimizes){ |
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STORM_LOG_ERROR("Inverting of properties not yet supported"); |
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//TODO
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return false; |
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} |
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bool success = false; |
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// Invoke preprocessing for subformula
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if(formula.getSubformula().isUntilFormula()){ |
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success = preprocess(formula.getSubformula().asUntilFormula(), info, currentObjective); |
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} else if(formula.getSubformula().isBoundedUntilFormula()){ |
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success = preprocess(formula.getSubformula().asBoundedUntilFormula(), info, currentObjective); |
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} else if(formula.getSubformula().isGloballyFormula()){ |
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success = preprocess(formula.getSubformula().asGloballyFormula(), info, currentObjective); |
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} else if(formula.getSubformula().isEventuallyFormula()){ |
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success = preprocess(formula.getSubformula().asEventuallyFormula(), info, currentObjective); |
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} |
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STORM_LOG_ERROR_COND(success, "No implementation for the subformula of " << formula << "."); |
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return success; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::RewardOperatorFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective) { |
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// Make sure that our decision for negative rewards is consistent with the formula
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if(info.areNegativeRewardsConsidered() != currentObjective.originalFormulaMinimizes){ |
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STORM_LOG_ERROR("Decided to consider " << (info.areNegativeRewardsConsidered() ? "negative" : "non-negative") << "rewards, but the formula " << formula << (currentObjective.originalFormulaMinimizes ? " minimizes" : "maximizes") << ". Reward objectives should either all minimize or all maximize."); |
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return false; |
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} |
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// Check if the reward model is uniquely specified
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if((formula.hasRewardModelName() && info.getModel().hasRewardModel(formula.getRewardModelName())) |
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|| info.getModel().hasUniqueRewardModel()){ |
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STORM_LOG_ERROR("The reward model is not unique and the formula " << formula << " does not specify a reward model."); |
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return false; |
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} |
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bool success = false; |
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// Invoke preprocessing for subformula
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if(formula.getSubformula().isEventuallyFormula()){ |
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success = preprocess(formula.getSubformula().asEventuallyFormula(), info, currentObjective, formula.getOptionalRewardModelName()); |
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} else if(formula.getSubformula().isCumulativeRewardFormula()) { |
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success = preprocess(formula.getSubformula().asCumulativeRewardFormula(), info, currentObjective, formula.getOptionalRewardModelName()); |
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} |
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STORM_LOG_ERROR_COND(success, "No implementation for the subformula of " << formula << "."); |
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return success; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::UntilFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective) { |
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bool success = false; |
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CheckTask<storm::logic::Formula> phiTask(formula.getLeftSubformula()); |
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CheckTask<storm::logic::Formula> psiTask(formula.getRightSubformula()); |
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storm::modelchecker::SparsePropositionalModelChecker<SparseMdpModelType> mc(info.getModel()); |
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success = mc.canHandle(phiTask) && mc.canHandle(psiTask); |
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STORM_LOG_ERROR_COND(success, "The subformulas of " << formula << " should be propositional."); |
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storm::storage::BitVector phiStates = mc.check(phiTask)->asExplicitQualitativeCheckResult().getTruthValuesVector(); |
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storm::storage::BitVector psiStates = mc.check(psiTask)->asExplicitQualitativeCheckResult().getTruthValuesVector(); |
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// modelUnfolder(info.model, (~phiStates) | psiStates)
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// info.model = modelUnfolder.info()
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// build info.stateMapping from modelUnfolder.stateMapping
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// build stateaction reward vector
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// insert it in the model information
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// TODO
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return success; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::BoundedUntilFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective) { |
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return preprocess(storm::logic::UntilFormula(formula.getLeftSubformula().asSharedPointer(), formula.getRightSubformula().asSharedPointer()), info, currentObjective); |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::GloballyFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective) { |
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//TODO
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STORM_LOG_ERROR("Globally not yet implemented"); |
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return false; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::EventuallyFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective, boost::optional<std::string> const& optionalRewardModelName) { |
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if(formula.isReachabilityProbabilityFormula()){ |
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return preprocess(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), formula.getSubformula().asSharedPointer()), info, currentObjective); |
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} |
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if (!formula.isReachabilityRewardFormula()){ |
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STORM_LOG_ERROR("The formula " << formula << " neither considers reachability Probabilities nor reachability rewards"); |
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return false; |
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} |
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//TODO
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STORM_LOG_ERROR("Rewards not yet implemented"); |
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return false; |
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} |
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template<typename SparseMdpModelType> |
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bool SparseMdpMultiObjectivePreprocessingHelper<SparseMdpModelType>::preprocess(storm::logic::CumulativeRewardFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective, boost::optional<std::string> const& optionalRewardModelName) { |
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//TODO
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STORM_LOG_ERROR("Rewards not yet implemented"); |
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return false; |
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} |
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template class SparseMdpMultiObjectivePreprocessingHelper<storm::models::sparse::Mdp<double>>; |
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} |
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} |
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} |
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#ifndef STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMDPMULTIOBJECTIVEPREPROCESSINGHELPER_H_ |
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#define STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMDPMULTIOBJECTIVEPREPROCESSINGHELPER_H_ |
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#include "src/logic/Formulas.h" |
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#include "src/modelchecker/multiobjective/helper/SparseMultiObjectiveModelCheckerInformation.h" |
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#include "src/storage/BitVector.h" |
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namespace storm { |
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namespace modelchecker { |
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namespace helper { |
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/* |
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* Helper Class to invoke the necessary preprocessing for multi objective model checking |
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*/ |
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template <class SparseMdpModelType> |
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class SparseMdpMultiObjectivePreprocessingHelper { |
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public: |
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typedef typename SparseMdpModelType::ValueType ValueType; |
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typedef typename SparseMdpModelType::RewardModelType RewardModelType; |
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typedef SparseMultiObjectiveModelCheckerInformation<SparseMdpModelType> Information; |
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/*! |
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* Preprocesses the given model w.r.t. the given formulas. |
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* @param originalModel The considered model |
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* @param originalFormula the considered formula. The subformulas should only contain one OperatorFormula at top level, i.e., the formula is simple. |
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*/ |
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static Information preprocess(storm::logic::MultiObjectiveFormula const& originalFormula, SparseMdpModelType originalModel); |
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private: |
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static bool gatherObjectiveInformation(storm::logic::MultiObjectiveFormula const& formula, Information& info); |
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static bool setStepBoundOfObjective(typename Information::ObjectiveInformation& currentObjective); |
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static bool setWhetherNegativeRewardsAreConsidered(Information& info); |
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/*! |
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* Apply the neccessary preprocessing for the given formula. |
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* @param formula the current (sub)formula |
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* @param info the current state of the preprocessing. |
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* @return true iff there was no error |
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*/ |
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// State formulas (will transform the formula and the reward model) |
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static bool preprocess(storm::logic::ProbabilityOperatorFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective); |
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static bool preprocess(storm::logic::RewardOperatorFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective); |
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// Path formulas (will transform the model) |
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static bool preprocess(storm::logic::UntilFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective); |
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static bool preprocess(storm::logic::BoundedUntilFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective); |
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static bool preprocess(storm::logic::GloballyFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective); |
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static bool preprocess(storm::logic::EventuallyFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective, boost::optional<std::string> const& optionalRewardModelName = boost::none); |
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static bool preprocess(storm::logic::CumulativeRewardFormula const& formula, Information& info, typename Information::ObjectiveInformation& currentObjective, boost::optional<std::string> const& optionalRewardModelName = boost::none); |
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static storm::storage::BitVector checkPropositionalFormula(storm::logic::Formula propFormula, SparseMdpModelType const& model); |
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}; |
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} |
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} |
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} |
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#endif /* STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMDPMULTIOBJECTIVEPREPROCESSINGHELPER_H_ */ |
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@ -0,0 +1,83 @@ |
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#ifndef STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMULTIOBJECTIVEMODELCHECKERINFORMATION_H_ |
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#define STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMULTIOBJECTIVEMODELCHECKERINFORMATION_H_ |
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#include <vector> |
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#include <memory> |
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#include "src/logic/Formulas.h" |
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namespace storm { |
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namespace modelchecker { |
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namespace helper { |
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template <class SparseModelType> |
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class SparseMultiObjectiveModelCheckerInformation { |
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public : |
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typedef typename SparseModelType::ValueType ValueType; |
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typedef typename SparseModelType::RewardModelType RewardModelType; |
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struct ObjectiveInformation { |
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std::shared_ptr<storm::logic::Formula const> originalFormula; |
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bool originalFormulaMinimizes; |
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boost::optional<double> threshold; |
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std::string rewardModelName; |
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boost::optional<uint_fast64_t> stepBound; |
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}; |
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SparseMultiObjectiveModelCheckerInformation(SparseModelType const& model) : model(model) { |
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//Intentionally left empty |
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} |
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SparseMultiObjectiveModelCheckerInformation(SparseModelType && model) : model(model) { |
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//Intentionally left empty |
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} |
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SparseModelType& getModel(){ |
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return model; |
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} |
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SparseModelType const& getModel() const { |
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return model; |
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} |
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std::vector<uint_fast64_t>& getNewToOldStateMapping(){ |
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return newToOldStateMapping; |
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} |
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std::vector<uint_fast64_t>const& getNewToOldStateMapping() const{ |
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return newToOldStateMapping; |
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} |
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bool areNegativeRewardsConsidered() { |
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return negativeRewardsConsidered; |
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} |
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void setNegativeRewardsConsidered(bool value){ |
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negativeRewardsConsidered = value; |
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} |
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std::vector<ObjectiveInformation>& getObjectives() { |
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return objectives; |
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} |
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std::vector<ObjectiveInformation>const& getObjectives() const { |
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return objectives; |
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} |
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private: |
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SparseModelType model; |
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std::vector<uint_fast64_t> newToOldStateMapping; |
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bool negativeRewardsConsidered; |
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std::vector<ObjectiveInformation> objectives; |
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}; |
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} |
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} |
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} |
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#endif /* STORM_MODELCHECKER_MULTIOBJECTIVE_HELPER_SPARSEMULTIOBJECTIVEMODELCHECKERINFORMATION_H_ */ |
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