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better modularity for multi-objective preprocessing

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TimQu 7 years ago
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4c0bda2664
11 changed files with 1063 additions and 958 deletions
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  1. 611
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
  2. 105
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h
  3. 105
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorResult.h
  4. 137
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorTask.h
  5. 103
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.cpp
  6. 33
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.h
  7. 424
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.cpp
  8. 105
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.h
  9. 95
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessorResult.h
  10. 230
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveRewardAnalysis.cpp
  11. 73
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveRewardAnalysis.h

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src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
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#include "storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h"
#include <algorithm>
#include <set>
#include "storm/models/sparse/Mdp.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/modelchecker/propositional/SparsePropositionalModelChecker.h"
#include "storm/modelchecker/prctl/helper/BaierUpperRewardBoundsComputer.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/storage/memorystructure/MemoryStructureBuilder.h"
#include "storm/storage/memorystructure/SparseModelMemoryProduct.h"
#include "storm/storage/expressions/ExpressionManager.h"
#include "storm/transformer/EndComponentEliminator.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/settings/SettingsManager.h"
#include "storm/settings/modules/GeneralSettings.h"
#include "storm/exceptions/InvalidPropertyException.h"
#include "storm/exceptions/UnexpectedException.h"
#include "storm/exceptions/NotImplementedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType SparseMultiObjectivePreprocessor<SparseModelType>::preprocess(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula) {
PreprocessorData data(originalModel);
//Invoke preprocessing on the individual objectives
for (auto const& subFormula : originalFormula.getSubformulas()) {
STORM_LOG_INFO("Preprocessing objective " << *subFormula<< ".");
data.objectives.push_back(std::make_shared<Objective<ValueType>>());
data.objectives.back()->originalFormula = subFormula;
data.finiteRewardCheckObjectives.resize(data.objectives.size(), false);
data.upperResultBoundObjectives.resize(data.objectives.size(), false);
if (data.objectives.back()->originalFormula->isOperatorFormula()) {
preprocessOperatorFormula(data.objectives.back()->originalFormula->asOperatorFormula(), data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not preprocess the subformula " << *subFormula << " of " << originalFormula << " because it is not supported");
}
}
// Incorporate the required memory into the state space
storm::storage::SparseModelMemoryProduct<ValueType> product = data.memory->product(originalModel);
std::shared_ptr<SparseModelType> preprocessedModel = std::dynamic_pointer_cast<SparseModelType>(product.build());
auto backwardTransitions = preprocessedModel->getBackwardTransitions();
// compute the end components of the model (if required)
bool endComponentAnalysisRequired = false;
for (auto& task : data.tasks) {
endComponentAnalysisRequired = endComponentAnalysisRequired || task->requiresEndComponentAnalysis();
}
if (endComponentAnalysisRequired) {
// TODO
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "End component analysis required but currently not implemented.");
}
for (auto& task : data.tasks) {
task->perform(*preprocessedModel);
}
// Remove reward models that are not needed anymore
std::set<std::string> relevantRewardModels;
for (auto const& obj : data.objectives) {
obj->formula->gatherReferencedRewardModels(relevantRewardModels);
}
preprocessedModel->restrictRewardModels(relevantRewardModels);
// Build the actual result
return buildResult(originalModel, originalFormula, data, preprocessedModel, backwardTransitions);
}
template <typename SparseModelType>
SparseMultiObjectivePreprocessor<SparseModelType>::PreprocessorData::PreprocessorData(SparseModelType const& model) : originalModel(model) {
memory = std::make_shared<storm::storage::MemoryStructure>(storm::storage::MemoryStructureBuilder<ValueType, RewardModelType>::buildTrivialMemoryStructure(model));
// The memoryLabelPrefix should not be a prefix of a state label of the given model to ensure uniqueness of label names
memoryLabelPrefix = "mem";
while (true) {
bool prefixIsUnique = true;
for (auto const& label : originalModel.getStateLabeling().getLabels()) {
if (memoryLabelPrefix.size() <= label.size()) {
if (std::mismatch(memoryLabelPrefix.begin(), memoryLabelPrefix.end(), label.begin()).first == memoryLabelPrefix.end()) {
prefixIsUnique = false;
memoryLabelPrefix = "_" + memoryLabelPrefix;
break;
}
}
}
if (prefixIsUnique) {
break;
}
}
// The rewardModelNamePrefix should not be a prefix of a reward model name of the given model to ensure uniqueness of reward model names
rewardModelNamePrefix = "obj";
while (true) {
bool prefixIsUnique = true;
for (auto const& label : originalModel.getStateLabeling().getLabels()) {
if (memoryLabelPrefix.size() <= label.size()) {
if (std::mismatch(memoryLabelPrefix.begin(), memoryLabelPrefix.end(), label.begin()).first == memoryLabelPrefix.end()) {
prefixIsUnique = false;
memoryLabelPrefix = "_" + memoryLabelPrefix;
break;
}
}
}
if (prefixIsUnique) {
break;
}
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessOperatorFormula(storm::logic::OperatorFormula const& formula, PreprocessorData& data) {
Objective<ValueType>& objective = *data.objectives.back();
// Check whether the complementary event is considered
objective.considersComplementaryEvent = formula.isProbabilityOperatorFormula() && formula.getSubformula().isGloballyFormula();
storm::logic::OperatorInformation opInfo;
if (formula.hasBound()) {
opInfo.bound = formula.getBound();
// Invert the bound (if necessary)
if (objective.considersComplementaryEvent) {
opInfo.bound->threshold = opInfo.bound->threshold.getManager().rational(storm::utility::one<storm::RationalNumber>()) - opInfo.bound->threshold;
switch (opInfo.bound->comparisonType) {
case storm::logic::ComparisonType::Greater:
opInfo.bound->comparisonType = storm::logic::ComparisonType::Less;
break;
case storm::logic::ComparisonType::GreaterEqual:
opInfo.bound->comparisonType = storm::logic::ComparisonType::LessEqual;
break;
case storm::logic::ComparisonType::Less:
opInfo.bound->comparisonType = storm::logic::ComparisonType::Greater;
break;
case storm::logic::ComparisonType::LessEqual:
opInfo.bound->comparisonType = storm::logic::ComparisonType::GreaterEqual;
break;
default:
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Current objective " << formula << " has unexpected comparison type");
}
}
if (storm::logic::isLowerBound(opInfo.bound->comparisonType)) {
opInfo.optimalityType = storm::solver::OptimizationDirection::Maximize;
} else {
opInfo.optimalityType = storm::solver::OptimizationDirection::Minimize;
}
STORM_LOG_WARN_COND(!formula.hasOptimalityType(), "Optimization direction of formula " << formula << " ignored as the formula also specifies a threshold.");
} else if (formula.hasOptimalityType()){
opInfo.optimalityType = formula.getOptimalityType();
// Invert the optimality type (if necessary)
if (objective.considersComplementaryEvent) {
opInfo.optimalityType = storm::solver::invert(opInfo.optimalityType.get());
}
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Objective " << formula << " does not specify whether to minimize or maximize");
}
if (formula.isProbabilityOperatorFormula()){
preprocessProbabilityOperatorFormula(formula.asProbabilityOperatorFormula(), opInfo, data);
} else if (formula.isRewardOperatorFormula()){
preprocessRewardOperatorFormula(formula.asRewardOperatorFormula(), opInfo, data);
} else if (formula.isTimeOperatorFormula()){
preprocessTimeOperatorFormula(formula.asTimeOperatorFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not preprocess the objective " << formula << " because it is not supported");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Probabilities are between zero and one
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
data.objectives.back()->upperResultBound = storm::utility::one<ValueType>();
if (formula.getSubformula().isUntilFormula()){
preprocessUntilFormula(formula.getSubformula().asUntilFormula(), opInfo, data);
} else if (formula.getSubformula().isBoundedUntilFormula()){
preprocessBoundedUntilFormula(formula.getSubformula().asBoundedUntilFormula(), opInfo, data);
} else if (formula.getSubformula().isGloballyFormula()){
preprocessGloballyFormula(formula.getSubformula().asGloballyFormula(), opInfo, data);
} else if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessRewardOperatorFormula(storm::logic::RewardOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
STORM_LOG_THROW((formula.hasRewardModelName() && data.originalModel.hasRewardModel(formula.getRewardModelName()))
|| (!formula.hasRewardModelName() && data.originalModel.hasUniqueRewardModel()), storm::exceptions::InvalidPropertyException, "The reward model is not unique or the formula " << formula << " does not specify an existing reward model.");
std::string rewardModelName;
if (formula.hasRewardModelName()) {
rewardModelName = formula.getRewardModelName();
STORM_LOG_THROW(data.originalModel.hasRewardModel(rewardModelName), storm::exceptions::InvalidPropertyException, "The reward model specified by formula " << formula << " does not exist in the model");
} else {
STORM_LOG_THROW(data.originalModel.hasUniqueRewardModel(), storm::exceptions::InvalidOperationException, "The formula " << formula << " does not specify a reward model name and the reward model is not unique.");
rewardModelName = data.originalModel.getRewardModels().begin()->first;
}
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isCumulativeRewardFormula()) {
preprocessCumulativeRewardFormula(formula.getSubformula().asCumulativeRewardFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isTotalRewardFormula()) {
preprocessTotalRewardFormula(opInfo, data, rewardModelName);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTimeOperatorFormula(storm::logic::TimeOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Time formulas are only supported for Markov automata
STORM_LOG_THROW(data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton), storm::exceptions::InvalidPropertyException, "Time operator formulas are only supported for Markov automata.");
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessUntilFormula(storm::logic::UntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, std::shared_ptr<storm::logic::Formula const> subformula) {
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(data.originalModel);
storm::storage::BitVector rightSubformulaResult = mc.check(formula.getRightSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
storm::storage::BitVector leftSubformulaResult = mc.check(formula.getLeftSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
// Check if the formula is already satisfied in the initial state because then the transformation to expected rewards will fail.
// TODO: Handle this case more properly
STORM_LOG_THROW((data.originalModel.getInitialStates() & rightSubformulaResult).empty(), storm::exceptions::NotImplementedException, "The Probability for the objective " << *data.objectives.back()->originalFormula << " is always one as the rhs of the until formula is true in the initial state. This (trivial) case is currently not implemented.");
// Create a memory structure that stores whether a non-PhiState or a PsiState has already been reached
storm::storage::MemoryStructureBuilder<ValueType, RewardModelType> builder(2, data.originalModel);
std::string relevantStatesLabel = data.memoryLabelPrefix + "_obj" + std::to_string(data.objectives.size()) + "_relevant";
builder.setLabel(0, relevantStatesLabel);
storm::storage::BitVector nonRelevantStates = ~leftSubformulaResult | rightSubformulaResult;
builder.setTransition(0, 0, ~nonRelevantStates);
builder.setTransition(0, 1, nonRelevantStates);
builder.setTransition(1, 1, storm::storage::BitVector(data.originalModel.getNumberOfStates(), true));
for (auto const& initState : data.originalModel.getInitialStates()) {
builder.setInitialMemoryState(initState, nonRelevantStates.get(initState) ? 1 : 0);
}
storm::storage::MemoryStructure objectiveMemory = builder.build();
data.memory = std::make_shared<storm::storage::MemoryStructure>(data.memory->product(objectiveMemory));
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
if (subformula == nullptr) {
subformula = std::make_shared<storm::logic::TotalRewardFormula>();
}
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(subformula, rewardModelName, opInfo);
auto relevantStatesFormula = std::make_shared<storm::logic::AtomicLabelFormula>(relevantStatesLabel);
data.tasks.push_back(std::make_shared<SparseMultiObjectivePreprocessorReachProbToTotalRewTask<SparseModelType>>(data.objectives.back(), relevantStatesFormula, formula.getRightSubformula().asSharedPointer()));
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessBoundedUntilFormula(storm::logic::BoundedUntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Check how to handle this query
if (formula.isMultiDimensional() || formula.getTimeBoundReference().isRewardBound()) {
data.objectives.back()->formula = std::make_shared<storm::logic::ProbabilityOperatorFormula>(formula.asSharedPointer(), opInfo);
} else if (!formula.hasLowerBound() || (!formula.isLowerBoundStrict() && storm::utility::isZero(formula.template getLowerBound<storm::RationalNumber>()))) {
std::shared_ptr<storm::logic::Formula const> subformula;
if (!formula.hasUpperBound()) {
// The formula is actually unbounded
subformula = std::make_shared<storm::logic::TotalRewardFormula>();
} else {
STORM_LOG_THROW(!data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton) || formula.getTimeBoundReference().isTimeBound(), storm::exceptions::InvalidPropertyException, "Bounded until formulas for Markov Automata are only allowed when time bounds are considered.");
storm::logic::TimeBound bound(formula.isUpperBoundStrict(), formula.getUpperBound());
subformula = std::make_shared<storm::logic::CumulativeRewardFormula>(bound, formula.getTimeBoundReference());
}
preprocessUntilFormula(storm::logic::UntilFormula(formula.getLeftSubformula().asSharedPointer(), formula.getRightSubformula().asSharedPointer()), opInfo, data, subformula);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Property " << formula << "is not supported");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// The formula is transformed to an until formula for the complementary event.
auto negatedSubformula = std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, formula.getSubformula().asSharedPointer());
preprocessUntilFormula(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), negatedSubformula), opInfo, data);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
if (formula.isReachabilityProbabilityFormula()){
preprocessUntilFormula(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), formula.getSubformula().asSharedPointer()), opInfo, data);
return;
}
// Analyze the subformula
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(data.originalModel);
storm::storage::BitVector subFormulaResult = mc.check(formula.getSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
// Create a memory structure that stores whether a target state has already been reached
storm::storage::MemoryStructureBuilder<ValueType, RewardModelType> builder(2, data.originalModel);
// Get a unique label that is not already present in the model
std::string relevantStatesLabel = data.memoryLabelPrefix + "_obj" + std::to_string(data.objectives.size()) + "_relevant";
builder.setLabel(0, relevantStatesLabel);
builder.setTransition(0, 0, ~subFormulaResult);
builder.setTransition(0, 1, subFormulaResult);
builder.setTransition(1, 1, storm::storage::BitVector(data.originalModel.getNumberOfStates(), true));
for (auto const& initState : data.originalModel.getInitialStates()) {
builder.setInitialMemoryState(initState, subFormulaResult.get(initState) ? 1 : 0);
}
storm::storage::MemoryStructure objectiveMemory = builder.build();
data.memory = std::make_shared<storm::storage::MemoryStructure>(data.memory->product(objectiveMemory));
auto relevantStatesFormula = std::make_shared<storm::logic::AtomicLabelFormula>(relevantStatesLabel);
std::string auxRewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
auto totalRewardFormula = std::make_shared<storm::logic::TotalRewardFormula>();
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(totalRewardFormula, auxRewardModelName, opInfo);
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
data.upperResultBoundObjectives.set(data.objectives.size() - 1, true);
if (formula.isReachabilityRewardFormula()) {
assert(optionalRewardModelName.is_initialized());
data.tasks.push_back(std::make_shared<SparseMultiObjectivePreprocessorReachRewToTotalRewTask<SparseModelType>>(data.objectives.back(), relevantStatesFormula, optionalRewardModelName.get()));
} else if (formula.isReachabilityTimeFormula() && data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton)) {
data.tasks.push_back(std::make_shared<SparseMultiObjectivePreprocessorReachTimeToTotalRewTask<SparseModelType>>(data.objectives.back(), relevantStatesFormula));
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The formula " << formula << " neither considers reachability probabilities nor reachability rewards " << (data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton) ? "nor reachability time" : "") << ". This is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessCumulativeRewardFormula(storm::logic::CumulativeRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
STORM_LOG_THROW(data.originalModel.isOfType(storm::models::ModelType::Mdp), storm::exceptions::InvalidPropertyException, "Cumulative reward formulas are not supported for the given model type.");
auto cumulativeRewardFormula = std::make_shared<storm::logic::CumulativeRewardFormula>(formula);
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(cumulativeRewardFormula, *optionalRewardModelName, opInfo);
bool onlyRewardBounds = true;
for (uint64_t i = 0; i < cumulativeRewardFormula->getDimension(); ++i) {
if (!cumulativeRewardFormula->getTimeBoundReference(i).isRewardBound()) {
onlyRewardBounds = false;
break;
}
}
if (onlyRewardBounds) {
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
data.upperResultBoundObjectives.set(data.objectives.size() - 1, true);
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTotalRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
auto totalRewardFormula = std::make_shared<storm::logic::TotalRewardFormula>();
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(totalRewardFormula, *optionalRewardModelName, opInfo);
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
data.upperResultBoundObjectives.set(data.objectives.size() - 1, true);
}
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType SparseMultiObjectivePreprocessor<SparseModelType>::buildResult(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula, PreprocessorData& data, std::shared_ptr<SparseModelType> const& preprocessedModel, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
ReturnType result(originalFormula, originalModel);
result.preprocessedModel = preprocessedModel;
for (auto& obj : data.objectives) {
result.objectives.push_back(std::move(*obj));
}
result.queryType = getQueryType(result.objectives);
setReward0States(result, backwardTransitions);
checkRewardFiniteness(result, data.finiteRewardCheckObjectives, backwardTransitions);
// We compute upper result bounds if the 'sound' option has been enabled
if (storm::settings::getModule<storm::settings::modules::GeneralSettings>().isSoundSet()) {
for (auto const& objIndex : data.upperResultBoundObjectives) {
result.objectives[objIndex].upperResultBound = computeUpperResultBound(result, objIndex, backwardTransitions);
}
}
return result;
}
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType::QueryType SparseMultiObjectivePreprocessor<SparseModelType>::getQueryType(std::vector<Objective<ValueType>> const& objectives) {
uint_fast64_t numOfObjectivesWithThreshold = 0;
for (auto& obj : objectives) {
if (obj.formula->hasBound()) {
++numOfObjectivesWithThreshold;
}
}
if (numOfObjectivesWithThreshold == objectives.size()) {
return ReturnType::QueryType::Achievability;
} else if (numOfObjectivesWithThreshold + 1 == objectives.size()) {
// Note: We do not want to consider a Pareto query when the total number of objectives is one.
return ReturnType::QueryType::Quantitative;
} else if (numOfObjectivesWithThreshold == 0) {
return ReturnType::QueryType::Pareto;
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Invalid Multi-objective query: The numer of qualitative objectives should be either 0 (Pareto query), 1 (quantitative query), or #objectives (achievability query).");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::setReward0States(ReturnType& result, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
uint_fast64_t stateCount = result.preprocessedModel->getNumberOfStates();
auto const& transitions = result.preprocessedModel->getTransitionMatrix();
std::vector<uint_fast64_t> const& groupIndices = transitions.getRowGroupIndices();
storm::storage::BitVector allStates(stateCount, true);
// Get the choices that yield non-zero reward
storm::storage::BitVector zeroRewardChoices(result.preprocessedModel->getNumberOfChoices(), true);
for (auto const& obj : result.objectives) {
if (obj.formula->isRewardOperatorFormula()) {
auto const& rewModel = result.preprocessedModel->getRewardModel(obj.formula->asRewardOperatorFormula().getRewardModelName());
zeroRewardChoices &= rewModel.getChoicesWithZeroReward(transitions);
}
}
// Get the states that have reward for at least one (or for all) choices assigned to it.
storm::storage::BitVector statesWithRewardForOneChoice = storm::storage::BitVector(stateCount, false);
storm::storage::BitVector statesWithRewardForAllChoices = storm::storage::BitVector(stateCount, true);
for (uint_fast64_t state = 0; state < stateCount; ++state) {
bool stateHasChoiceWithReward = false;
bool stateHasChoiceWithoutReward = false;
uint_fast64_t const& groupEnd = groupIndices[state + 1];
for (uint_fast64_t choice = groupIndices[state]; choice < groupEnd; ++choice) {
if (zeroRewardChoices.get(choice)) {
stateHasChoiceWithoutReward = true;
} else {
stateHasChoiceWithReward = true;
}
}
if (stateHasChoiceWithReward) {
statesWithRewardForOneChoice.set(state, true);
}
if (stateHasChoiceWithoutReward) {
statesWithRewardForAllChoices.set(state, false);
}
}
// get the states for which there is a scheduler yielding reward zero
result.reward0EStates = storm::utility::graph::performProbGreater0A(transitions, groupIndices, backwardTransitions, allStates, statesWithRewardForAllChoices, false, 0, zeroRewardChoices);
result.reward0EStates.complement();
result.reward0AStates = storm::utility::graph::performProb0A(backwardTransitions, allStates, statesWithRewardForOneChoice);
assert(result.reward0AStates.isSubsetOf(result.reward0EStates));
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::checkRewardFiniteness(ReturnType& result, storm::storage::BitVector const& finiteRewardCheckObjectives, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
result.rewardFinitenessType = ReturnType::RewardFinitenessType::AllFinite;
auto const& transitions = result.preprocessedModel->getTransitionMatrix();
std::vector<uint_fast64_t> const& groupIndices = transitions.getRowGroupIndices();
storm::storage::BitVector maxRewardsToCheck(result.preprocessedModel->getNumberOfChoices(), true);
storm::storage::BitVector minRewardsToCheck(result.preprocessedModel->getNumberOfChoices(), true);
for (auto const& objIndex : finiteRewardCheckObjectives) {
STORM_LOG_ASSERT(result.objectives[objIndex].formula->isRewardOperatorFormula(), "Objective needs to be checked for finite reward but has no reward operator.");
auto const& rewModel = result.preprocessedModel->getRewardModel(result.objectives[objIndex].formula->asRewardOperatorFormula().getRewardModelName());
auto unrelevantChoices = rewModel.getChoicesWithZeroReward(transitions);
// For (upper) reward bounded cumulative reward formulas, we do not need to consider the choices where boundReward is collected.
if (result.objectives[objIndex].formula->getSubformula().isCumulativeRewardFormula()) {
auto const& timeBoundReference = result.objectives[objIndex].formula->getSubformula().asCumulativeRewardFormula().getTimeBoundReference();
// Only reward bounded formulas need a finiteness check
assert(timeBoundReference.isRewardBound());
auto const& rewModelOfBound = result.preprocessedModel->getRewardModel(timeBoundReference.getRewardName());
unrelevantChoices |= ~rewModelOfBound.getChoicesWithZeroReward(transitions);
}
if (storm::solver::minimize(result.objectives[objIndex].formula->getOptimalityType())) {
minRewardsToCheck &= unrelevantChoices;
} else {
maxRewardsToCheck &= unrelevantChoices;
}
}
maxRewardsToCheck.complement();
minRewardsToCheck.complement();
// Check reward finiteness under all schedulers
storm::storage::BitVector allStates(result.preprocessedModel->getNumberOfStates(), true);
if (storm::utility::graph::checkIfECWithChoiceExists(transitions, backwardTransitions, allStates, maxRewardsToCheck | minRewardsToCheck)) {
// Check whether there is a scheduler yielding infinite reward for a maximizing objective
if (storm::utility::graph::checkIfECWithChoiceExists(transitions, backwardTransitions, allStates, maxRewardsToCheck)) {
result.rewardFinitenessType = ReturnType::RewardFinitenessType::Infinite;
} else {
// Check whether there is a scheduler under which all rewards are finite.
result.rewardLessInfinityEStates = storm::utility::graph::performProb1E(transitions, groupIndices, backwardTransitions, allStates, result.reward0EStates);
if ((result.rewardLessInfinityEStates.get() & result.preprocessedModel->getInitialStates()).empty()) {
// There is no scheduler that induces finite reward for the initial state
result.rewardFinitenessType = ReturnType::RewardFinitenessType::Infinite;
} else {
result.rewardFinitenessType = ReturnType::RewardFinitenessType::ExistsParetoFinite;
}
}
} else {
result.rewardLessInfinityEStates = allStates;
}
}
template<typename SparseModelType>
boost::optional<typename SparseModelType::ValueType> SparseMultiObjectivePreprocessor<SparseModelType>::computeUpperResultBound(ReturnType const& result, uint64_t objIndex, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
boost::optional<ValueType> upperBound;
if (!result.originalModel.isOfType(storm::models::ModelType::Mdp)) {
return upperBound;
}
auto const& transitions = result.preprocessedModel->getTransitionMatrix();
if (result.objectives[objIndex].formula->isRewardOperatorFormula()) {
auto const& rewModel = result.preprocessedModel->getRewardModel(result.objectives[objIndex].formula->asRewardOperatorFormula().getRewardModelName());
auto actionRewards = rewModel.getTotalRewardVector(transitions);
if (result.objectives[objIndex].formula->getSubformula().isTotalRewardFormula() || result.objectives[objIndex].formula->getSubformula().isCumulativeRewardFormula()) {
// We have to eliminate ECs here to treat zero-reward ECs
storm::storage::BitVector allStates(result.preprocessedModel->getNumberOfStates(), true);
// Get the set of states from which no reward is reachable
auto nonZeroRewardStates = rewModel.getStatesWithZeroReward(transitions);
nonZeroRewardStates.complement();
auto expRewGreater0EStates = storm::utility::graph::performProbGreater0E(backwardTransitions, allStates, nonZeroRewardStates);
auto zeroRewardChoices = rewModel.getChoicesWithZeroReward(transitions);
auto ecElimRes = storm::transformer::EndComponentEliminator<ValueType>::transform(transitions, expRewGreater0EStates, zeroRewardChoices, ~allStates);
allStates.resize(ecElimRes.matrix.getRowGroupCount());
storm::storage::BitVector outStates(allStates.size(), false);
std::vector<ValueType> rew0StateProbs;
rew0StateProbs.reserve(ecElimRes.matrix.getRowCount());
for (uint64_t state = 0; state < allStates.size(); ++ state) {
for (uint64_t choice = ecElimRes.matrix.getRowGroupIndices()[state]; choice < ecElimRes.matrix.getRowGroupIndices()[state + 1]; ++choice) {
// Check whether the choice lead to a state with expRew 0 in the original model
bool isOutChoice = false;
uint64_t originalModelChoice = ecElimRes.newToOldRowMapping[choice];
for (auto const& entry : transitions.getRow(originalModelChoice)) {
if (!expRewGreater0EStates.get(entry.getColumn())) {
isOutChoice = true;
outStates.set(state, true);
rew0StateProbs.push_back(storm::utility::one<ValueType>() - ecElimRes.matrix.getRowSum(choice));
assert (!storm::utility::isZero(rew0StateProbs.back()));
break;
}
}
if (!isOutChoice) {
rew0StateProbs.push_back(storm::utility::zero<ValueType>());
}
}
}
// An upper reward bound can only be computed if it is below infinity
if (storm::utility::graph::performProb1A(ecElimRes.matrix, ecElimRes.matrix.getRowGroupIndices(), ecElimRes.matrix.transpose(true), allStates, outStates).full()) {
std::vector<ValueType> rewards;
rewards.reserve(ecElimRes.matrix.getRowCount());
for (auto row : ecElimRes.newToOldRowMapping) {
rewards.push_back(actionRewards[row]);
}
storm::modelchecker::helper::BaierUpperRewardBoundsComputer<ValueType> baier(ecElimRes.matrix, rewards, rew0StateProbs);
if (upperBound) {
upperBound = std::min(upperBound.get(), baier.computeUpperBound());
} else {
upperBound = baier.computeUpperBound();
}
}
}
}
if (upperBound) {
STORM_LOG_INFO("Computed upper result bound " << upperBound.get() << " for objective " << *result.objectives[objIndex].formula << ".");
} else {
STORM_LOG_WARN("Could not compute upper result bound for objective " << *result.objectives[objIndex].formula);
}
return upperBound;
}
template class SparseMultiObjectivePreprocessor<storm::models::sparse::Mdp<double>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::MarkovAutomaton<double>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::Mdp<storm::RationalNumber>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::MarkovAutomaton<storm::RationalNumber>>;
}
}
}

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src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h
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#pragma once
#include <memory>
#include <string>
#include "storm/logic/Formulas.h"
#include "storm/storage/BitVector.h"
#include "storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorResult.h"
#include "storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorTask.h"
#include "storm/storage/memorystructure/MemoryStructure.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
/*
* This class invokes the necessary preprocessing for the constraint based multi-objective model checking algorithm
*/
template <class SparseModelType>
class SparseMultiObjectivePreprocessor {
public:
typedef typename SparseModelType::ValueType ValueType;
typedef typename SparseModelType::RewardModelType RewardModelType;
typedef SparseMultiObjectivePreprocessorResult<SparseModelType> ReturnType;
/*!
* Preprocesses the given model w.r.t. the given formulas
* @param originalModel The considered model
* @param originalFormula the considered formula. The subformulas should only contain one OperatorFormula at top level.
*/
static ReturnType preprocess(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula);
private:
struct PreprocessorData {
SparseModelType const& originalModel;
std::vector<std::shared_ptr<Objective<ValueType>>> objectives;
std::vector<std::shared_ptr<SparseMultiObjectivePreprocessorTask<SparseModelType>>> tasks;
std::shared_ptr<storm::storage::MemoryStructure> memory;
// Indices of the objectives that require a check for finite reward
storm::storage::BitVector finiteRewardCheckObjectives;
// Indices of the objectives for which we need to compute an upper bound for the result
storm::storage::BitVector upperResultBoundObjectives;
std::string memoryLabelPrefix;
std::string rewardModelNamePrefix;
PreprocessorData(SparseModelType const& model);
};
/*!
* Apply the neccessary preprocessing for the given formula.
* @param formula the current (sub)formula
* @param opInfo the information of the resulting operator formula
* @param data the current data. The currently processed objective is located at data.objectives.back()
* @param optionalRewardModelName the reward model name that is considered for the formula (if available)
*/
static void preprocessOperatorFormula(storm::logic::OperatorFormula const& formula, PreprocessorData& data);
static void preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessRewardOperatorFormula(storm::logic::RewardOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessTimeOperatorFormula(storm::logic::TimeOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessUntilFormula(storm::logic::UntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, std::shared_ptr<storm::logic::Formula const> subformula = nullptr);
static void preprocessBoundedUntilFormula(storm::logic::BoundedUntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessCumulativeRewardFormula(storm::logic::CumulativeRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessTotalRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none); // The total reward formula itself does not need to be provided as it is unique.
/*!
* Builds the result from preprocessing
*/
static ReturnType buildResult(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula, PreprocessorData& data, std::shared_ptr<SparseModelType> const& preprocessedModel, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Returns the query type
*/
static typename ReturnType::QueryType getQueryType(std::vector<Objective<ValueType>> const& objectives);
/*!
* Computes the set of states that have zero expected reward w.r.t. all expected reward objectives
*/
static void setReward0States(ReturnType& result, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Checks whether the occurring expected rewards are finite and sets the RewardFinitenessType accordingly
* Returns the set of states for which a scheduler exists that induces finite reward for all objectives
*/
static void checkRewardFiniteness(ReturnType& result, storm::storage::BitVector const& finiteRewardCheckObjectives, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Finds an upper bound for the expected reward of the objective with the given index (assuming it considers an expected reward objective)
*/
static boost::optional<ValueType> computeUpperResultBound(ReturnType const& result, uint64_t objIndex, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
};
}
}
}

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#pragma once
#include <vector>
#include <memory>
#include <boost/optional.hpp>
#include "storm/logic/Formulas.h"
#include "storm/modelchecker/multiobjective/Objective.h"
#include "storm/storage/BitVector.h"
#include "storm/exceptions/UnexpectedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
template <class SparseModelType>
struct SparseMultiObjectivePreprocessorResult {
enum class QueryType { Achievability, Quantitative, Pareto };
enum class RewardFinitenessType {
AllFinite, // The expected reward is finite for all objectives and all schedulers
ExistsParetoFinite, // There is a Pareto optimal scheduler yielding finite rewards for all objectives
Infinite // All Pareto optimal schedulers yield infinite reward for at least one objective
};
// Original data
storm::logic::MultiObjectiveFormula const& originalFormula;
SparseModelType const& originalModel;
// The preprocessed model and objectives
std::shared_ptr<SparseModelType> preprocessedModel;
std::vector<Objective<typename SparseModelType::ValueType>> objectives;
// Data about the query
QueryType queryType;
RewardFinitenessType rewardFinitenessType;
// The states of the preprocessed model for which...
storm::storage::BitVector reward0EStates; // ... there is a scheduler such that all expected reward objectives have value zero
storm::storage::BitVector reward0AStates; // ... all schedulers induce value 0 for all expected reward objectives
boost::optional<storm::storage::BitVector> rewardLessInfinityEStates; // ... there is a scheduler yielding finite reward for all expected reward objectives
// Note that other types of objectives (e.g., reward bounded reachability objectives) are not considered.
// Encodes a superset of the set of choices of preprocessedModel that are part of an end component (if given).
//boost::optional<storm::storage::BitVector> ecChoicesHint;
SparseMultiObjectivePreprocessorResult(storm::logic::MultiObjectiveFormula const& originalFormula, SparseModelType const& originalModel) : originalFormula(originalFormula), originalModel(originalModel) {
// Intentionally left empty
}
bool containsOnlyTrivialObjectives() const {
// Trivial objectives are either total reward formulas or single-dimensional step or time bounded cumulative reward formulas
for (auto const& obj : objectives) {
if (obj.formula->isRewardOperatorFormula() && obj.formula->getSubformula().isTotalRewardFormula()) {
continue;
}
if (obj.formula->isRewardOperatorFormula() && obj.formula->getSubformula().isCumulativeRewardFormula()) {
auto const& subf = obj.formula->getSubformula().asCumulativeRewardFormula();
if (!subf.isMultiDimensional() && (subf.getTimeBoundReference().isTimeBound() || subf.getTimeBoundReference().isStepBound())) {
continue;
}
}
// Reaching this point means that the objective is considered as non-trivial
return false;
}
return true;
}
void printToStream(std::ostream& out) const {
out << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
out << " Multi-objective Query " << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
out << std::endl;
out << "Original Formula: " << std::endl;
out << "--------------------------------------------------------------" << std::endl;
out << "\t" << originalFormula << std::endl;
out << std::endl;
out << "Objectives:" << std::endl;
out << "--------------------------------------------------------------" << std::endl;
for (auto const& obj : objectives) {
obj.printToStream(out);
}
out << "--------------------------------------------------------------" << std::endl;
out << std::endl;
out << "Original Model Information:" << std::endl;
originalModel.printModelInformationToStream(out);
out << std::endl;
out << "Preprocessed Model Information:" << std::endl;
preprocessedModel->printModelInformationToStream(out);
out << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
}
friend std::ostream& operator<<(std::ostream& out, SparseMultiObjectivePreprocessorResult<SparseModelType> const& ret) {
ret.printToStream(out);
return out;
}
};
}
}
}

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#pragma once
#include <boost/optional.hpp>
#include <memory>
#include "storm/logic/Formula.h"
#include "storm/logic/Bound.h"
#include "storm/solver/OptimizationDirection.h"
#include "storm/modelchecker/multiobjective/Objective.h"
#include "storm/modelchecker/propositional/SparsePropositionalModelChecker.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/models/sparse/Mdp.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/utility/vector.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
template <typename SparseModelType>
class SparseMultiObjectivePreprocessorTask {
public:
SparseMultiObjectivePreprocessorTask(std::shared_ptr<Objective<typename SparseModelType::ValueType>> const& objective) : objective(objective) {
// intentionally left empty
}
virtual ~SparseMultiObjectivePreprocessorTask() = default;
virtual void perform(SparseModelType& preprocessedModel) const = 0;
virtual bool requiresEndComponentAnalysis() const {
return false;
}
protected:
std::shared_ptr<Objective<typename SparseModelType::ValueType>> objective;
};
// Transforms reachability probabilities to total expected rewards by adding a rewardModel
// such that one reward is given whenever a goal state is reached from a relevant state
template <typename SparseModelType>
class SparseMultiObjectivePreprocessorReachProbToTotalRewTask : public SparseMultiObjectivePreprocessorTask<SparseModelType> {
public:
SparseMultiObjectivePreprocessorReachProbToTotalRewTask(std::shared_ptr<Objective<typename SparseModelType::ValueType>> const& objective, std::shared_ptr<storm::logic::Formula const> const& relevantStateFormula, std::shared_ptr<storm::logic::Formula const> const& goalStateFormula) : SparseMultiObjectivePreprocessorTask<SparseModelType>(objective), relevantStateFormula(relevantStateFormula), goalStateFormula(goalStateFormula) {
// Intentionally left empty
}
virtual void perform(SparseModelType& preprocessedModel) const override {
// build stateAction reward vector that gives (one*transitionProbability) reward whenever a transition leads from a relevantState to a goalState
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(preprocessedModel);
storm::storage::BitVector relevantStates = mc.check(*relevantStateFormula)->asExplicitQualitativeCheckResult().getTruthValuesVector();
storm::storage::BitVector goalStates = mc.check(*goalStateFormula)->asExplicitQualitativeCheckResult().getTruthValuesVector();
std::vector<typename SparseModelType::ValueType> objectiveRewards(preprocessedModel.getTransitionMatrix().getRowCount(), storm::utility::zero<typename SparseModelType::ValueType>());
for (auto const& state : relevantStates) {
for (uint_fast64_t row = preprocessedModel.getTransitionMatrix().getRowGroupIndices()[state]; row < preprocessedModel.getTransitionMatrix().getRowGroupIndices()[state + 1]; ++row) {
objectiveRewards[row] = preprocessedModel.getTransitionMatrix().getConstrainedRowSum(row, goalStates);
}
}
STORM_LOG_ASSERT(this->objective->formula->isRewardOperatorFormula(), "No reward operator formula.");
STORM_LOG_ASSERT(this->objective->formula->asRewardOperatorFormula().hasRewardModelName(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->formula->asRewardOperatorFormula().getRewardModelName(), typename SparseModelType::RewardModelType(boost::none, std::move(objectiveRewards)));
}
private:
std::shared_ptr<storm::logic::Formula const> relevantStateFormula;
std::shared_ptr<storm::logic::Formula const> goalStateFormula;
};
// Transforms expected reachability rewards to total expected rewards by adding a rewardModel
// such that non-relevant states get reward zero
template <typename SparseModelType>
class SparseMultiObjectivePreprocessorReachRewToTotalRewTask : public SparseMultiObjectivePreprocessorTask<SparseModelType> {
public:
SparseMultiObjectivePreprocessorReachRewToTotalRewTask(std::shared_ptr<Objective<typename SparseModelType::ValueType>> const& objective, std::shared_ptr<storm::logic::Formula const> const& relevantStateFormula, std::string const& originalRewardModelName) : SparseMultiObjectivePreprocessorTask<SparseModelType>(objective), relevantStateFormula(relevantStateFormula), originalRewardModelName(originalRewardModelName) {
// Intentionally left empty
}
virtual void perform(SparseModelType& preprocessedModel) const override {
// build stateAction reward vector that only gives reward for relevant states
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(preprocessedModel);
storm::storage::BitVector nonRelevantStates = ~mc.check(*relevantStateFormula)->asExplicitQualitativeCheckResult().getTruthValuesVector();
typename SparseModelType::RewardModelType objectiveRewards = preprocessedModel.getRewardModel(originalRewardModelName);
objectiveRewards.reduceToStateBasedRewards(preprocessedModel.getTransitionMatrix(), false);
if (objectiveRewards.hasStateRewards()) {
storm::utility::vector::setVectorValues(objectiveRewards.getStateRewardVector(), nonRelevantStates, storm::utility::zero<typename SparseModelType::ValueType>());
}
if (objectiveRewards.hasStateActionRewards()) {
for (auto state : nonRelevantStates) {
std::fill_n(objectiveRewards.getStateActionRewardVector().begin() + preprocessedModel.getTransitionMatrix().getRowGroupIndices()[state], preprocessedModel.getTransitionMatrix().getRowGroupSize(state), storm::utility::zero<typename SparseModelType::ValueType>());
}
}
STORM_LOG_ASSERT(this->objective->formula->isRewardOperatorFormula(), "No reward operator formula.");
STORM_LOG_ASSERT(this->objective->formula->asRewardOperatorFormula().hasRewardModelName(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->formula->asRewardOperatorFormula().getRewardModelName(), std::move(objectiveRewards));
}
private:
std::shared_ptr<storm::logic::Formula const> relevantStateFormula;
std::string originalRewardModelName;
};
// Transforms expected reachability time to total expected rewards by adding a rewardModel
// such that every time step done from a relevant state yields one reward
template <typename SparseModelType>
class SparseMultiObjectivePreprocessorReachTimeToTotalRewTask : public SparseMultiObjectivePreprocessorTask<SparseModelType> {
public:
SparseMultiObjectivePreprocessorReachTimeToTotalRewTask(std::shared_ptr<Objective<typename SparseModelType::ValueType>> const& objective, std::shared_ptr<storm::logic::Formula const> const& relevantStateFormula) : SparseMultiObjectivePreprocessorTask<SparseModelType>(objective), relevantStateFormula(relevantStateFormula) {
// Intentionally left empty
}
virtual void perform(SparseModelType& preprocessedModel) const override {
// build stateAction reward vector that only gives reward for relevant states
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(preprocessedModel);
storm::storage::BitVector relevantStates = mc.check(*relevantStateFormula)->asExplicitQualitativeCheckResult().getTruthValuesVector();
std::vector<typename SparseModelType::ValueType> timeRewards(preprocessedModel.getNumberOfStates(), storm::utility::zero<typename SparseModelType::ValueType>());
storm::utility::vector::setVectorValues(timeRewards, dynamic_cast<storm::models::sparse::MarkovAutomaton<typename SparseModelType::ValueType> const&>(preprocessedModel).getMarkovianStates() & relevantStates, storm::utility::one<typename SparseModelType::ValueType>());
STORM_LOG_ASSERT(this->objective->formula->isRewardOperatorFormula(), "No reward operator formula.");
STORM_LOG_ASSERT(this->objective->formula->asRewardOperatorFormula().hasRewardModelName(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->formula->asRewardOperatorFormula().getRewardModelName(), typename SparseModelType::RewardModelType(std::move(timeRewards)));
}
private:
std::shared_ptr<storm::logic::Formula const> relevantStateFormula;
};
}
}
}

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src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.cpp
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#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.h"
#include "storm/models/sparse/Mdp.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/storage/memorystructure/MemoryStructureBuilder.h"
#include "storm/storage/memorystructure/SparseModelMemoryProduct.h"
#include "storm/logic/Formulas.h"
#include "storm/logic/FragmentSpecification.h"
#include "storm/modelchecker/propositional/SparsePropositionalModelChecker.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/utility/macros.h"
#include "storm/exceptions/NotImplementedException.h"
#include "storm/exceptions/NotSupportedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
template <class SparseModelType>
storm::storage::MemoryStructure getGoalMemory(SparseModelType const& model, storm::logic::Formula const& propositionalGoalStateFormula) {
STORM_LOG_THROW(propositionalGoalStateFormula.isInFragment(storm::logic::propositional()), storm::exceptions::NotSupportedException, "The subformula " << propositionalGoalStateFormula << " should be propositional.");
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(model);
storm::storage::BitVector goalStates = mc.check(propositionalGoalStateFormula)->asExplicitQualitativeCheckResult().getTruthValuesVector();
// Check if the formula is already satisfied for all initial states. In such a case the trivial memory structure suffices.
if (model.getInitialStates().isSubsetOf(goalStates)) {
STORM_LOG_INFO("One objective is already satisfied for all initial states.");
return storm::storage::MemoryStructureBuilder<typename SparseModelType::ValueType, typename SparseModelType::RewardModelType>::buildTrivialMemoryStructure(model);
}
// Create a memory structure that stores whether a goal state has already been reached
storm::storage::MemoryStructureBuilder<typename SparseModelType::ValueType, typename SparseModelType::RewardModelType> builder(2, model);
builder.setTransition(0, 0, ~goalStates);
builder.setTransition(0, 1, goalStates);
builder.setTransition(1, 1, storm::storage::BitVector(model.getNumberOfStates(), true));
for (auto const& initState : model.getInitialStates()) {
builder.setInitialMemoryState(initState, goalStates.get(initState) ? 1 : 0);
}
return builder.build();
}
template <class SparseModelType>
storm::storage::MemoryStructure getUntilFormulaMemory(SparseModelType const& model, storm::logic::Formula const& leftSubFormula, storm::logic::Formula const& rightSubFormula) {
auto notLeftOrRight = std::make_shared<storm::logic::BinaryBooleanStateFormula>(storm::logic::BinaryBooleanStateFormula::OperatorType::Or,
std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, leftSubFormula.asSharedPointer()),
rightSubFormula.asSharedPointer());
return getGoalMemory<SparseModelType>(model, *notLeftOrRight);
}
template <class SparseModelType>
std::shared_ptr<SparseModelType> SparseMultiObjectiveMemoryIncorporation<SparseModelType>::incorporateGoalMemory(SparseModelType const& model, storm::logic::MultiObjectiveFormula const& formula) {
storm::storage::MemoryStructure memory = storm::storage::MemoryStructureBuilder<ValueType, RewardModelType>::buildTrivialMemoryStructure(model);
for (auto const& subFormula : formula.getSubformulas()) {
STORM_LOG_THROW(subFormula->isOperatorFormula(), storm::exceptions::NotSupportedException, "The given Formula " << *subFormula << " is not supported.");
auto const& subsubFormula = subFormula->asOperatorFormula().getSubformula();
if (subsubFormula.isEventuallyFormula()) {
memory = memory.product(getGoalMemory(model, subsubFormula.asEventuallyFormula().getSubformula()));
} else if (subsubFormula.isUntilFormula()) {
memory = memory.product(getUntilFormulaMemory(model, subsubFormula.asUntilFormula().getLeftSubformula(), subsubFormula.asUntilFormula().getRightSubformula()));
} else if (subsubFormula.isBoundedUntilFormula()) {
// For bounded formulas it is only reasonable to add the goal memory if it considers a single upper step/time bound.
auto const& buf = subsubFormula.asBoundedUntilFormula();
if (!buf.isMultiDimensional() && !buf.getTimeBoundReference().isRewardBound() && (!buf.hasLowerBound() || (!buf.isLowerBoundStrict() && storm::utility::isZero(buf.template getLowerBound<storm::RationalNumber>())))) {
memory = memory.product(getUntilFormulaMemory(model, buf.getLeftSubformula(), buf.getRightSubformula()));
}
} else if (subsubFormula.isGloballyFormula()) {
auto notPhi = std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, subsubFormula.asGloballyFormula().getSubformula().asSharedPointer());
memory = memory.product(getGoalMemory(model, *notPhi));
} else {
STORM_LOG_THROW(subFormula->isOperatorFormula(), storm::exceptions::NotSupportedException, "The given Formula " << subsubFormula << " is not supported.");
}
}
storm::storage::SparseModelMemoryProduct<ValueType> product = memory.product(model);
return std::dynamic_pointer_cast<SparseModelType>(product.build());
}
template <class SparseModelType>
std::shared_ptr<SparseModelType> SparseMultiObjectiveMemoryIncorporation<SparseModelType>::incorporateFullMemory(SparseModelType const& model, uint64_t memoryStates) {
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "not implemented");
return nullptr;
}
template class SparseMultiObjectiveMemoryIncorporation<storm::models::sparse::Mdp<double>>;
template class SparseMultiObjectiveMemoryIncorporation<storm::models::sparse::MarkovAutomaton<double>>;
template class SparseMultiObjectiveMemoryIncorporation<storm::models::sparse::Mdp<storm::RationalNumber>>;
template class SparseMultiObjectiveMemoryIncorporation<storm::models::sparse::MarkovAutomaton<storm::RationalNumber>>;
}
}
}
}

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src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.h
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#pragma once
#include <memory>
#include "storm/storage/memorystructure/MemoryStructure.h"
#include "storm/logic/MultiObjectiveFormula.h"
#include "storm/logic/Formula.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
template <class SparseModelType>
class SparseMultiObjectiveMemoryIncorporation {
typedef typename SparseModelType::ValueType ValueType;
typedef typename SparseModelType::RewardModelType RewardModelType;
public:
static std::shared_ptr<SparseModelType> incorporateGoalMemory(SparseModelType const& model, storm::logic::MultiObjectiveFormula const& formula);
static std::shared_ptr<SparseModelType> incorporateFullMemory(SparseModelType const& model, uint64_t memoryStates);
};
}
}
}
}

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src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.cpp
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#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.h"
#include <algorithm>
#include <set>
#include "storm/models/sparse/Mdp.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.h"
#include "storm/modelchecker/propositional/SparsePropositionalModelChecker.h"
#include "storm/modelchecker/prctl/helper/BaierUpperRewardBoundsComputer.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/storage/expressions/ExpressionManager.h"
#include "storm/transformer/EndComponentEliminator.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/settings/SettingsManager.h"
#include "storm/settings/modules/GeneralSettings.h"
#include "storm/exceptions/InvalidPropertyException.h"
#include "storm/exceptions/UnexpectedException.h"
#include "storm/exceptions/NotImplementedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType SparseMultiObjectivePreprocessor<SparseModelType>::preprocess(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula) {
auto model = SparseMultiObjectiveMemoryIncorporation<SparseModelType>::incorporateGoalMemory(originalModel, originalFormula);
PreprocessorData data(model);
//Invoke preprocessing on the individual objectives
for (auto const& subFormula : originalFormula.getSubformulas()) {
STORM_LOG_INFO("Preprocessing objective " << *subFormula<< ".");
data.objectives.push_back(std::make_shared<Objective<ValueType>>());
data.objectives.back()->originalFormula = subFormula;
data.finiteRewardCheckObjectives.resize(data.objectives.size(), false);
data.upperResultBoundObjectives.resize(data.objectives.size(), false);
if (data.objectives.back()->originalFormula->isOperatorFormula()) {
preprocessOperatorFormula(data.objectives.back()->originalFormula->asOperatorFormula(), data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not preprocess the subformula " << *subFormula << " of " << originalFormula << " because it is not supported");
}
}
// Remove reward models that are not needed anymore
std::set<std::string> relevantRewardModels;
for (auto const& obj : data.objectives) {
obj->formula->gatherReferencedRewardModels(relevantRewardModels);
}
data.model->restrictRewardModels(relevantRewardModels);
// Build the actual result
return buildResult(originalModel, originalFormula, data);
}
template <typename SparseModelType>
SparseMultiObjectivePreprocessor<SparseModelType>::PreprocessorData::PreprocessorData(std::shared_ptr<SparseModelType> model) : model(model) {
// The rewardModelNamePrefix should not be a prefix of a reward model name of the given model to ensure uniqueness of new reward model names
rewardModelNamePrefix = "obj";
while (true) {
bool prefixIsUnique = true;
for (auto const& rewardModels : model->getRewardModels()) {
if (rewardModelNamePrefix.size() <= rewardModels.first.size()) {
if (std::mismatch(rewardModelNamePrefix.begin(), rewardModelNamePrefix.end(), rewardModels.first.begin()).first == rewardModelNamePrefix.end()) {
prefixIsUnique = false;
rewardModelNamePrefix = "_" + rewardModelNamePrefix;
break;
}
}
}
if (prefixIsUnique) {
break;
}
}
}
storm::logic::OperatorInformation getOperatorInformation(storm::logic::OperatorFormula const& formula, bool considerComplementaryEvent) {
storm::logic::OperatorInformation opInfo;
if (formula.hasBound()) {
opInfo.bound = formula.getBound();
// Invert the bound (if necessary)
if (considerComplementaryEvent) {
opInfo.bound->threshold = opInfo.bound->threshold.getManager().rational(storm::utility::one<storm::RationalNumber>()) - opInfo.bound->threshold;
switch (opInfo.bound->comparisonType) {
case storm::logic::ComparisonType::Greater:
opInfo.bound->comparisonType = storm::logic::ComparisonType::Less;
break;
case storm::logic::ComparisonType::GreaterEqual:
opInfo.bound->comparisonType = storm::logic::ComparisonType::LessEqual;
break;
case storm::logic::ComparisonType::Less:
opInfo.bound->comparisonType = storm::logic::ComparisonType::Greater;
break;
case storm::logic::ComparisonType::LessEqual:
opInfo.bound->comparisonType = storm::logic::ComparisonType::GreaterEqual;
break;
default:
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Current objective " << formula << " has unexpected comparison type");
}
}
if (storm::logic::isLowerBound(opInfo.bound->comparisonType)) {
opInfo.optimalityType = storm::solver::OptimizationDirection::Maximize;
} else {
opInfo.optimalityType = storm::solver::OptimizationDirection::Minimize;
}
STORM_LOG_WARN_COND(!formula.hasOptimalityType(), "Optimization direction of formula " << formula << " ignored as the formula also specifies a threshold.");
} else if (formula.hasOptimalityType()){
opInfo.optimalityType = formula.getOptimalityType();
// Invert the optimality type (if necessary)
if (considerComplementaryEvent) {
opInfo.optimalityType = storm::solver::invert(opInfo.optimalityType.get());
}
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Objective " << formula << " does not specify whether to minimize or maximize");
}
return opInfo;
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessOperatorFormula(storm::logic::OperatorFormula const& formula, PreprocessorData& data) {
Objective<ValueType>& objective = *data.objectives.back();
// Check whether the complementary event is considered
objective.considersComplementaryEvent = formula.isProbabilityOperatorFormula() && formula.getSubformula().isGloballyFormula();
// Extract the operator information from the formula and potentially invert it for the complementary event
storm::logic::OperatorInformation opInfo = getOperatorInformation(formula, objective.considersComplementaryEvent);
if (formula.isProbabilityOperatorFormula()){
preprocessProbabilityOperatorFormula(formula.asProbabilityOperatorFormula(), opInfo, data);
} else if (formula.isRewardOperatorFormula()){
preprocessRewardOperatorFormula(formula.asRewardOperatorFormula(), opInfo, data);
} else if (formula.isTimeOperatorFormula()){
preprocessTimeOperatorFormula(formula.asTimeOperatorFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not preprocess the objective " << formula << " because it is not supported");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Probabilities are between zero and one
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
data.objectives.back()->upperResultBound = storm::utility::one<ValueType>();
if (formula.getSubformula().isUntilFormula()){
preprocessUntilFormula(formula.getSubformula().asUntilFormula(), opInfo, data);
} else if (formula.getSubformula().isBoundedUntilFormula()){
preprocessBoundedUntilFormula(formula.getSubformula().asBoundedUntilFormula(), opInfo, data);
} else if (formula.getSubformula().isGloballyFormula()){
preprocessGloballyFormula(formula.getSubformula().asGloballyFormula(), opInfo, data);
} else if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessRewardOperatorFormula(storm::logic::RewardOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
STORM_LOG_THROW((formula.hasRewardModelName() && data.model->hasRewardModel(formula.getRewardModelName()))
|| (!formula.hasRewardModelName() && data.model->hasUniqueRewardModel()), storm::exceptions::InvalidPropertyException, "The reward model is not unique or the formula " << formula << " does not specify an existing reward model.");
std::string rewardModelName;
if (formula.hasRewardModelName()) {
rewardModelName = formula.getRewardModelName();
STORM_LOG_THROW(data.model->hasRewardModel(rewardModelName), storm::exceptions::InvalidPropertyException, "The reward model specified by formula " << formula << " does not exist in the model");
} else {
STORM_LOG_THROW(data.model->hasUniqueRewardModel(), storm::exceptions::InvalidOperationException, "The formula " << formula << " does not specify a reward model name and the reward model is not unique.");
rewardModelName = data.model->getRewardModels().begin()->first;
}
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isCumulativeRewardFormula()) {
preprocessCumulativeRewardFormula(formula.getSubformula().asCumulativeRewardFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isTotalRewardFormula()) {
preprocessTotalRewardFormula(opInfo, data, rewardModelName);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTimeOperatorFormula(storm::logic::TimeOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Time formulas are only supported for Markov automata
STORM_LOG_THROW(data.model->isOfType(storm::models::ModelType::MarkovAutomaton), storm::exceptions::InvalidPropertyException, "Time operator formulas are only supported for Markov automata.");
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), opInfo, data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessUntilFormula(storm::logic::UntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, std::shared_ptr<storm::logic::Formula const> subformula) {
// Try to transform the formula to expected total (or cumulative) rewards
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(*data.model);
storm::storage::BitVector rightSubformulaResult = mc.check(formula.getRightSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
// Check if the formula is already satisfied in the initial state because then the transformation to expected rewards will fail.
// TODO: Handle this case more properly
STORM_LOG_THROW((data.model->getInitialStates() & rightSubformulaResult).empty(), storm::exceptions::NotImplementedException, "The Probability for the objective " << *data.objectives.back()->originalFormula << " is always one as the rhs of the until formula is true in the initial state. This (trivial) case is currently not implemented.");
// Whenever a state that violates the left subformula or satisfies the right subformula is reached, the objective is 'decided', i.e., no more reward should be collected from there
storm::storage::BitVector notLeftOrRight = mc.check(formula.getLeftSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
notLeftOrRight.complement();
notLeftOrRight |=rightSubformulaResult;
// Get the states that are reachable from a notLeftOrRight state
storm::storage::BitVector allStates(data.model->getNumberOfStates(), true), noStates(data.model->getNumberOfStates(), false);
storm::storage::BitVector reachableFromGoal = storm::utility::graph::getReachableStates(data.model->getTransitionMatrix(), notLeftOrRight, allStates, noStates);
// Get the states that are reachable from an initial state, stopping at the states reachable from goal
storm::storage::BitVector reachableFromInit = storm::utility::graph::getReachableStates(data.model->getTransitionMatrix(), data.model->getInitialStates(), ~notLeftOrRight, reachableFromGoal);
// Exclude the actual notLeftOrRight states from the states that are reachable from init
reachableFromInit &= ~notLeftOrRight;
// If we can reach a state that is reachable from goal, but which is not a goal state, it means that the transformation to expected rewards is not possible.
if ((reachableFromInit & reachableFromGoal).empty()) {
STORM_LOG_INFO("Objective " << data.objectives.back()->originalFormula << " is transformed to an expected total/cumulative reward property.");
// Transform to expected total rewards:
// build stateAction reward vector that gives (one*transitionProbability) reward whenever a transition leads from a reachableFromInit state to a goalState
std::vector<typename SparseModelType::ValueType> objectiveRewards(data.model->getTransitionMatrix().getRowCount(), storm::utility::zero<typename SparseModelType::ValueType>());
for (auto const& state : reachableFromInit) {
for (uint_fast64_t row = data.model->getTransitionMatrix().getRowGroupIndices()[state]; row < data.model->getTransitionMatrix().getRowGroupIndices()[state + 1]; ++row) {
objectiveRewards[row] = data.model->getTransitionMatrix().getConstrainedRowSum(row, rightSubformulaResult);
}
}
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
data.model->addRewardModel(rewardModelName, typename SparseModelType::RewardModelType(boost::none, std::move(objectiveRewards)));
if (subformula == nullptr) {
subformula = std::make_shared<storm::logic::TotalRewardFormula>();
}
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(subformula, rewardModelName, opInfo);
} else {
STORM_LOG_INFO("Objective " << data.objectives.back()->originalFormula << " can not be transformed to an expected total/cumulative reward property.");
data.objectives.back()->formula = std::make_shared<storm::logic::ProbabilityOperatorFormula>(formula.asSharedPointer(), opInfo);
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessBoundedUntilFormula(storm::logic::BoundedUntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// Check how to handle this query
if (formula.isMultiDimensional() || formula.getTimeBoundReference().isRewardBound()) {
STORM_LOG_INFO("Objective " << data.objectives.back()->originalFormula << " is not transformed to an expected cumulative reward property.");
data.objectives.back()->formula = std::make_shared<storm::logic::ProbabilityOperatorFormula>(formula.asSharedPointer(), opInfo);
} else if (!formula.hasLowerBound() || (!formula.isLowerBoundStrict() && storm::utility::isZero(formula.template getLowerBound<storm::RationalNumber>()))) {
std::shared_ptr<storm::logic::Formula const> subformula;
if (!formula.hasUpperBound()) {
// The formula is actually unbounded
subformula = std::make_shared<storm::logic::TotalRewardFormula>();
} else {
STORM_LOG_THROW(!data.model->isOfType(storm::models::ModelType::MarkovAutomaton) || formula.getTimeBoundReference().isTimeBound(), storm::exceptions::InvalidPropertyException, "Bounded until formulas for Markov Automata are only allowed when time bounds are considered.");
storm::logic::TimeBound bound(formula.isUpperBoundStrict(), formula.getUpperBound());
subformula = std::make_shared<storm::logic::CumulativeRewardFormula>(bound, formula.getTimeBoundReference());
}
preprocessUntilFormula(storm::logic::UntilFormula(formula.getLeftSubformula().asSharedPointer(), formula.getRightSubformula().asSharedPointer()), opInfo, data, subformula);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Property " << formula << "is not supported");
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data) {
// The formula is transformed to an until formula for the complementary event.
auto negatedSubformula = std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, formula.getSubformula().asSharedPointer());
preprocessUntilFormula(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), negatedSubformula), opInfo, data);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
if (formula.isReachabilityProbabilityFormula()){
preprocessUntilFormula(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), formula.getSubformula().asSharedPointer()), opInfo, data);
return;
}
// Analyze the subformula
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(*data.model);
storm::storage::BitVector subFormulaResult = mc.check(formula.getSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector();
// Get the states that are reachable from a goal state
storm::storage::BitVector allStates(data.model->getNumberOfStates(), true), noStates(data.model->getNumberOfStates(), false);
storm::storage::BitVector reachableFromGoal = storm::utility::graph::getReachableStates(data.model->getTransitionMatrix(), subFormulaResult, allStates, noStates);
// Get the states that are reachable from an initial state, stopping at the states reachable from goal
storm::storage::BitVector reachableFromInit = storm::utility::graph::getReachableStates(data.model->getTransitionMatrix(), data.model->getInitialStates(), allStates, reachableFromGoal);
// Exclude the actual goal states from the states that are reachable from an initial state
reachableFromInit &= ~subFormulaResult;
// If we can reach a state that is reachable from goal but which is not a goal state, it means that the transformation to expected total rewards is not possible.
if ((reachableFromInit & reachableFromGoal).empty()) {
STORM_LOG_INFO("Objective " << data.objectives.back()->originalFormula << " is transformed to an expected total reward property.");
// Transform to expected total rewards:
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
auto totalRewardFormula = std::make_shared<storm::logic::TotalRewardFormula>();
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(totalRewardFormula, rewardModelName, opInfo);
if (formula.isReachabilityRewardFormula()) {
// build stateAction reward vector that only gives reward for states that are reachable from init
assert(optionalRewardModelName.is_initialized());
typename SparseModelType::RewardModelType objectiveRewards = data.model->getRewardModel(optionalRewardModelName.get());
objectiveRewards.reduceToStateBasedRewards(data.model->getTransitionMatrix(), false);
if (objectiveRewards.hasStateRewards()) {
storm::utility::vector::setVectorValues(objectiveRewards.getStateRewardVector(), reachableFromGoal, storm::utility::zero<typename SparseModelType::ValueType>());
}
if (objectiveRewards.hasStateActionRewards()) {
for (auto state : reachableFromGoal) {
std::fill_n(objectiveRewards.getStateActionRewardVector().begin() + data.model->getTransitionMatrix().getRowGroupIndices()[state], data.model->getTransitionMatrix().getRowGroupSize(state), storm::utility::zero<typename SparseModelType::ValueType>());
}
}
data.model->addRewardModel(rewardModelName, std::move(objectiveRewards));
} else if (formula.isReachabilityTimeFormula() && data.model->isOfType(storm::models::ModelType::MarkovAutomaton)) {
// build stateAction reward vector that only gives reward for relevant states
std::vector<typename SparseModelType::ValueType> timeRewards(data.model->getNumberOfStates(), storm::utility::zero<typename SparseModelType::ValueType>());
storm::utility::vector::setVectorValues(timeRewards, dynamic_cast<storm::models::sparse::MarkovAutomaton<typename SparseModelType::ValueType> const&>(*data.model).getMarkovianStates() & reachableFromInit, storm::utility::one<typename SparseModelType::ValueType>());
data.model->addRewardModel(rewardModelName, typename SparseModelType::RewardModelType(std::move(timeRewards)));
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The formula " << formula << " neither considers reachability probabilities nor reachability rewards " << (data.model->isOfType(storm::models::ModelType::MarkovAutomaton) ? "nor reachability time" : "") << ". This is not supported.");
}
} else {
STORM_LOG_INFO("Objective " << data.objectives.back()->originalFormula << " can not be transformed to an expected total/cumulative reward property.");
if (formula.isReachabilityRewardFormula()) {
assert(optionalRewardModelName.is_initialized());
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(formula.asSharedPointer(), optionalRewardModelName.get(), opInfo);
} else if (formula.isReachabilityTimeFormula() && data.model->isOfType(storm::models::ModelType::MarkovAutomaton)) {
data.objectives.back()->formula = std::make_shared<storm::logic::TimeOperatorFormula>(formula.asSharedPointer(), opInfo);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The formula " << formula << " neither considers reachability probabilities nor reachability rewards " << (data.model->isOfType(storm::models::ModelType::MarkovAutomaton) ? "nor reachability time" : "") << ". This is not supported.");
}
}
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessCumulativeRewardFormula(storm::logic::CumulativeRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
STORM_LOG_THROW(data.model->isOfType(storm::models::ModelType::Mdp), storm::exceptions::InvalidPropertyException, "Cumulative reward formulas are not supported for the given model type.");
auto cumulativeRewardFormula = std::make_shared<storm::logic::CumulativeRewardFormula>(formula);
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(cumulativeRewardFormula, *optionalRewardModelName, opInfo);
bool onlyRewardBounds = true;
for (uint64_t i = 0; i < cumulativeRewardFormula->getDimension(); ++i) {
if (!cumulativeRewardFormula->getTimeBoundReference(i).isRewardBound()) {
onlyRewardBounds = false;
break;
}
}
if (onlyRewardBounds) {
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTotalRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
auto totalRewardFormula = std::make_shared<storm::logic::TotalRewardFormula>();
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(totalRewardFormula, *optionalRewardModelName, opInfo);
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
}
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType SparseMultiObjectivePreprocessor<SparseModelType>::buildResult(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula, PreprocessorData& data) {
ReturnType result(originalFormula, originalModel);
auto backwardTransitions = data.model->getBackwardTransitions();
result.preprocessedModel = data.model;
for (auto& obj : data.objectives) {
result.objectives.push_back(std::move(*obj));
}
result.queryType = getQueryType(result.objectives);
result.maybeInfiniteRewardObjectives = std::move(data.finiteRewardCheckObjectives);
return result;
}
template<typename SparseModelType>
typename SparseMultiObjectivePreprocessor<SparseModelType>::ReturnType::QueryType SparseMultiObjectivePreprocessor<SparseModelType>::getQueryType(std::vector<Objective<ValueType>> const& objectives) {
uint_fast64_t numOfObjectivesWithThreshold = 0;
for (auto& obj : objectives) {
if (obj.formula->hasBound()) {
++numOfObjectivesWithThreshold;
}
}
if (numOfObjectivesWithThreshold == objectives.size()) {
return ReturnType::QueryType::Achievability;
} else if (numOfObjectivesWithThreshold + 1 == objectives.size()) {
// Note: We do not want to consider a Pareto query when the total number of objectives is one.
return ReturnType::QueryType::Quantitative;
} else if (numOfObjectivesWithThreshold == 0) {
return ReturnType::QueryType::Pareto;
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Invalid Multi-objective query: The numer of qualitative objectives should be either 0 (Pareto query), 1 (quantitative query), or #objectives (achievability query).");
}
}
template class SparseMultiObjectivePreprocessor<storm::models::sparse::Mdp<double>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::MarkovAutomaton<double>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::Mdp<storm::RationalNumber>>;
template class SparseMultiObjectivePreprocessor<storm::models::sparse::MarkovAutomaton<storm::RationalNumber>>;
}
}
}
}

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#pragma once
#include <memory>
#include <string>
#include "storm/logic/Formulas.h"
#include "storm/storage/BitVector.h"
#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessorResult.h"
#include "storm/storage/memorystructure/MemoryStructure.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
/*
* This class invokes the necessary preprocessing for the constraint based multi-objective model checking algorithm
*/
template <class SparseModelType>
class SparseMultiObjectivePreprocessor {
public:
typedef typename SparseModelType::ValueType ValueType;
typedef typename SparseModelType::RewardModelType RewardModelType;
typedef SparseMultiObjectivePreprocessorResult<SparseModelType> ReturnType;
/*!
* Preprocesses the given model w.r.t. the given formulas
* @param originalModel The considered model
* @param originalFormula the considered formula. The subformulas should only contain one OperatorFormula at top level.
*/
static ReturnType preprocess(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula);
private:
struct PreprocessorData {
std::shared_ptr<SparseModelType> model;
std::vector<std::shared_ptr<Objective<ValueType>>> objectives;
// Indices of the objectives that require a check for finite reward
storm::storage::BitVector finiteRewardCheckObjectives;
// Indices of the objectives for which we need to compute an upper bound for the result
storm::storage::BitVector upperResultBoundObjectives;
std::string rewardModelNamePrefix;
PreprocessorData(std::shared_ptr<SparseModelType> model);
};
/*!
* Apply the neccessary preprocessing for the given formula.
* @param formula the current (sub)formula
* @param opInfo the information of the resulting operator formula
* @param data the current data. The currently processed objective is located at data.objectives.back()
* @param optionalRewardModelName the reward model name that is considered for the formula (if available)
*/
static void preprocessOperatorFormula(storm::logic::OperatorFormula const& formula, PreprocessorData& data);
static void preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessRewardOperatorFormula(storm::logic::RewardOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessTimeOperatorFormula(storm::logic::TimeOperatorFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessUntilFormula(storm::logic::UntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, std::shared_ptr<storm::logic::Formula const> subformula = nullptr);
static void preprocessBoundedUntilFormula(storm::logic::BoundedUntilFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data);
static void preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessCumulativeRewardFormula(storm::logic::CumulativeRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessTotalRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none); // The total reward formula itself does not need to be provided as it is unique.
/*!
* Builds the result from preprocessing
*/
static ReturnType buildResult(SparseModelType const& originalModel, storm::logic::MultiObjectiveFormula const& originalFormula, PreprocessorData& data);
/*!
* Returns the query type
*/
static typename ReturnType::QueryType getQueryType(std::vector<Objective<ValueType>> const& objectives);
/*!
* Computes the set of states that have zero expected reward w.r.t. all expected reward objectives
*/
static void setReward0States(ReturnType& result, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Checks whether the occurring expected rewards are finite and sets the RewardFinitenessType accordingly
* Returns the set of states for which a scheduler exists that induces finite reward for all objectives
*/
static void checkRewardFiniteness(ReturnType& result, storm::storage::BitVector const& finiteRewardCheckObjectives, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Finds an upper bound for the expected reward of the objective with the given index (assuming it considers an expected reward objective)
*/
static boost::optional<ValueType> computeUpperResultBound(ReturnType const& result, uint64_t objIndex, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
};
}
}
}
}

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#pragma once
#include <vector>
#include <memory>
#include <boost/optional.hpp>
#include "storm/logic/Formulas.h"
#include "storm/modelchecker/multiobjective/Objective.h"
#include "storm/storage/BitVector.h"
#include "storm/exceptions/UnexpectedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
template <class SparseModelType>
struct SparseMultiObjectivePreprocessorResult {
enum class QueryType { Achievability, Quantitative, Pareto };
// Original data
storm::logic::MultiObjectiveFormula const& originalFormula;
SparseModelType const& originalModel;
// The preprocessed model and objectives
std::shared_ptr<SparseModelType> preprocessedModel;
std::vector<Objective<typename SparseModelType::ValueType>> objectives;
// Data about the query
QueryType queryType;
// Indices of the objectives that can potentially yield infinite reward
storm::storage::BitVector maybeInfiniteRewardObjectives;
SparseMultiObjectivePreprocessorResult(storm::logic::MultiObjectiveFormula const& originalFormula, SparseModelType const& originalModel) : originalFormula(originalFormula), originalModel(originalModel) {
// Intentionally left empty
}
bool containsOnlyTrivialObjectives() const {
// Trivial objectives are either total reward formulas or single-dimensional step or time bounded cumulative reward formulas
for (auto const& obj : objectives) {
if (obj.formula->isRewardOperatorFormula() && obj.formula->getSubformula().isTotalRewardFormula()) {
continue;
}
if (obj.formula->isRewardOperatorFormula() && obj.formula->getSubformula().isCumulativeRewardFormula()) {
auto const& subf = obj.formula->getSubformula().asCumulativeRewardFormula();
if (!subf.isMultiDimensional() && (subf.getTimeBoundReference().isTimeBound() || subf.getTimeBoundReference().isStepBound())) {
continue;
}
}
// Reaching this point means that the objective is considered as non-trivial
return false;
}
return true;
}
void printToStream(std::ostream& out) const {
out << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
out << " Multi-objective Query " << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
out << std::endl;
out << "Original Formula: " << std::endl;
out << "--------------------------------------------------------------" << std::endl;
out << "\t" << originalFormula << std::endl;
out << std::endl;
out << "Objectives:" << std::endl;
out << "--------------------------------------------------------------" << std::endl;
for (auto const& obj : objectives) {
obj.printToStream(out);
}
out << "--------------------------------------------------------------" << std::endl;
out << std::endl;
out << "Original Model Information:" << std::endl;
originalModel.printModelInformationToStream(out);
out << std::endl;
out << "Preprocessed Model Information:" << std::endl;
preprocessedModel->printModelInformationToStream(out);
out << std::endl;
out << "---------------------------------------------------------------------------------------------------------------------------------------" << std::endl;
}
friend std::ostream& operator<<(std::ostream& out, SparseMultiObjectivePreprocessorResult<SparseModelType> const& ret) {
ret.printToStream(out);
return out;
}
};
}
}
}
}

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src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveRewardAnalysis.cpp
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#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveRewardAnalysis.h"
#include <algorithm>
#include <set>
#include <storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorResult.h>
#include "storm/models/sparse/Mdp.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveMemoryIncorporation.h"
#include "storm/modelchecker/propositional/SparsePropositionalModelChecker.h"
#include "storm/modelchecker/prctl/helper/BaierUpperRewardBoundsComputer.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/storage/expressions/ExpressionManager.h"
#include "storm/transformer/EndComponentEliminator.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/settings/SettingsManager.h"
#include "storm/settings/modules/GeneralSettings.h"
#include "storm/exceptions/InvalidPropertyException.h"
#include "storm/exceptions/UnexpectedException.h"
#include "storm/exceptions/NotImplementedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
template<typename SparseModelType>
typename SparseMultiObjectiveRewardAnalysis<SparseModelType>::ReturnType SparseMultiObjectiveRewardAnalysis<SparseModelType>::analyze(storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult) {
ReturnType result;
auto backwardTransitions = preprocessorResult.preprocessedModel->getBackwardTransitions();
setReward0States(result, preprocessorResult, backwardTransitions);
checkRewardFiniteness(result, preprocessorResult, backwardTransitions);
return result;
}
template<typename SparseModelType>
void SparseMultiObjectiveRewardAnalysis<SparseModelType>::setReward0States(ReturnType& result, storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
uint_fast64_t stateCount = preprocessorResult.preprocessedModel->getNumberOfStates();
auto const& transitions = preprocessorResult.preprocessedModel->getTransitionMatrix();
std::vector<uint_fast64_t> const& groupIndices = transitions.getRowGroupIndices();
storm::storage::BitVector allStates(stateCount, true);
// Get the choices that yield non-zero reward
storm::storage::BitVector zeroRewardChoices(preprocessorResult.preprocessedModel->getNumberOfChoices(), true);
for (auto const& obj : preprocessorResult.objectives) {
if (obj.formula->isRewardOperatorFormula()) {
STORM_LOG_WARN_COND(obj.formula->getSubformula().isTotalRewardFormula() || obj.formula->getSubformula().isCumulativeRewardFormula(), "Analyzing reachability reward formulas is not supported properly.");
auto const& rewModel = preprocessorResult.preprocessedModel->getRewardModel(obj.formula->asRewardOperatorFormula().getRewardModelName());
zeroRewardChoices &= rewModel.getChoicesWithZeroReward(transitions);
}
}
// Get the states that have reward for at least one (or for all) choices assigned to it.
storm::storage::BitVector statesWithRewardForOneChoice = storm::storage::BitVector(stateCount, false);
storm::storage::BitVector statesWithRewardForAllChoices = storm::storage::BitVector(stateCount, true);
for (uint_fast64_t state = 0; state < stateCount; ++state) {
bool stateHasChoiceWithReward = false;
bool stateHasChoiceWithoutReward = false;
uint_fast64_t const& groupEnd = groupIndices[state + 1];
for (uint_fast64_t choice = groupIndices[state]; choice < groupEnd; ++choice) {
if (zeroRewardChoices.get(choice)) {
stateHasChoiceWithoutReward = true;
} else {
stateHasChoiceWithReward = true;
}
}
if (stateHasChoiceWithReward) {
statesWithRewardForOneChoice.set(state, true);
}
if (stateHasChoiceWithoutReward) {
statesWithRewardForAllChoices.set(state, false);
}
}
// get the states for which there is a scheduler yielding reward zero
result.reward0EStates = storm::utility::graph::performProbGreater0A(transitions, groupIndices, backwardTransitions, allStates, statesWithRewardForAllChoices, false, 0, zeroRewardChoices);
result.reward0EStates.complement();
result.reward0AStates = storm::utility::graph::performProb0A(backwardTransitions, allStates, statesWithRewardForOneChoice);
assert(result.reward0AStates.isSubsetOf(result.reward0EStates));
}
template<typename SparseModelType>
void SparseMultiObjectiveRewardAnalysis<SparseModelType>::checkRewardFiniteness(ReturnType& result, storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
result.rewardFinitenessType = RewardFinitenessType::AllFinite;
auto const& transitions = preprocessorResult.preprocessedModel->getTransitionMatrix();
std::vector<uint_fast64_t> const& groupIndices = transitions.getRowGroupIndices();
storm::storage::BitVector maxRewardsToCheck(preprocessorResult.preprocessedModel->getNumberOfChoices(), true);
storm::storage::BitVector minRewardsToCheck(preprocessorResult.preprocessedModel->getNumberOfChoices(), true);
for (auto const& objIndex : preprocessorResult.maybeInfiniteRewardObjectives) {
STORM_LOG_ASSERT(preprocessorResult.objectives[objIndex].formula->isRewardOperatorFormula(), "Objective needs to be checked for finite reward but has no reward operator.");
auto const& rewModel = preprocessorResult.preprocessedModel->getRewardModel(preprocessorResult.objectives[objIndex].formula->asRewardOperatorFormula().getRewardModelName());
auto unrelevantChoices = rewModel.getChoicesWithZeroReward(transitions);
// For (upper) reward bounded cumulative reward formulas, we do not need to consider the choices where boundReward is collected.
if (preprocessorResult.objectives[objIndex].formula->getSubformula().isCumulativeRewardFormula()) {
auto const& timeBoundReference = preprocessorResult.objectives[objIndex].formula->getSubformula().asCumulativeRewardFormula().getTimeBoundReference();
// Only reward bounded formulas need a finiteness check
assert(timeBoundReference.isRewardBound());
auto const& rewModelOfBound = preprocessorResult.preprocessedModel->getRewardModel(timeBoundReference.getRewardName());
unrelevantChoices |= ~rewModelOfBound.getChoicesWithZeroReward(transitions);
}
if (storm::solver::minimize(preprocessorResult.objectives[objIndex].formula->getOptimalityType())) {
minRewardsToCheck &= unrelevantChoices;
} else {
maxRewardsToCheck &= unrelevantChoices;
}
}
maxRewardsToCheck.complement();
minRewardsToCheck.complement();
// Check reward finiteness under all schedulers
storm::storage::BitVector allStates(preprocessorResult.preprocessedModel->getNumberOfStates(), true);
if (storm::utility::graph::checkIfECWithChoiceExists(transitions, backwardTransitions, allStates, maxRewardsToCheck | minRewardsToCheck)) {
// Check whether there is a scheduler yielding infinite reward for a maximizing objective
if (storm::utility::graph::checkIfECWithChoiceExists(transitions, backwardTransitions, allStates, maxRewardsToCheck)) {
result.rewardFinitenessType = RewardFinitenessType::Infinite;
} else {
// Check whether there is a scheduler under which all rewards are finite.
result.rewardLessInfinityEStates = storm::utility::graph::performProb1E(transitions, groupIndices, backwardTransitions, allStates, result.reward0EStates);
if ((result.rewardLessInfinityEStates.get() & preprocessorResult.preprocessedModel->getInitialStates()).empty()) {
// There is no scheduler that induces finite reward for the initial state
result.rewardFinitenessType = RewardFinitenessType::Infinite;
} else {
result.rewardFinitenessType = RewardFinitenessType::ExistsParetoFinite;
}
}
} else {
result.rewardLessInfinityEStates = allStates;
}
}
template<typename SparseModelType>
void SparseMultiObjectiveRewardAnalysis<SparseModelType>::computeUpperResultBound(SparseModelType const& model, storm::modelchecker::multiobjective::Objective<ValueType>& objective, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
STORM_LOG_INFO_COND(!objective.upperResultBound.is_initialized(), "Tried to find an upper result bound for an objective, but a result bound is already there.");
if (model.isOfType(storm::models::ModelType::Mdp)) {
auto const& transitions = model.getTransitionMatrix();
if (objective.formula->isRewardOperatorFormula()) {
auto const& rewModel = model.getRewardModel(objective.formula->asRewardOperatorFormula().getRewardModelName());
auto actionRewards = rewModel.getTotalRewardVector(transitions);
if (objective.formula->getSubformula().isTotalRewardFormula() || objective.formula->getSubformula().isCumulativeRewardFormula()) {
// We have to eliminate ECs here to treat zero-reward ECs
storm::storage::BitVector allStates(model.getNumberOfStates(), true);
// Get the set of states from which no reward is reachable
auto nonZeroRewardStates = rewModel.getStatesWithZeroReward(transitions);
nonZeroRewardStates.complement();
auto expRewGreater0EStates = storm::utility::graph::performProbGreater0E(backwardTransitions, allStates, nonZeroRewardStates);
auto zeroRewardChoices = rewModel.getChoicesWithZeroReward(transitions);
auto ecElimRes = storm::transformer::EndComponentEliminator<ValueType>::transform(transitions, expRewGreater0EStates, zeroRewardChoices, ~allStates);
allStates.resize(ecElimRes.matrix.getRowGroupCount());
storm::storage::BitVector outStates(allStates.size(), false);
std::vector<ValueType> rew0StateProbs;
rew0StateProbs.reserve(ecElimRes.matrix.getRowCount());
for (uint64_t state = 0; state < allStates.size(); ++ state) {
for (uint64_t choice = ecElimRes.matrix.getRowGroupIndices()[state]; choice < ecElimRes.matrix.getRowGroupIndices()[state + 1]; ++choice) {
// Check whether the choice lead to a state with expRew 0 in the original model
bool isOutChoice = false;
uint64_t originalModelChoice = ecElimRes.newToOldRowMapping[choice];
for (auto const& entry : transitions.getRow(originalModelChoice)) {
if (!expRewGreater0EStates.get(entry.getColumn())) {
isOutChoice = true;
outStates.set(state, true);
rew0StateProbs.push_back(storm::utility::one<ValueType>() - ecElimRes.matrix.getRowSum(choice));
assert (!storm::utility::isZero(rew0StateProbs.back()));
break;
}
}
if (!isOutChoice) {
rew0StateProbs.push_back(storm::utility::zero<ValueType>());
}
}
}
// An upper reward bound can only be computed if it is below infinity
if (storm::utility::graph::performProb1A(ecElimRes.matrix, ecElimRes.matrix.getRowGroupIndices(), ecElimRes.matrix.transpose(true), allStates, outStates).full()) {
std::vector<ValueType> rewards;
rewards.reserve(ecElimRes.matrix.getRowCount());
for (auto row : ecElimRes.newToOldRowMapping) {
rewards.push_back(actionRewards[row]);
}
storm::modelchecker::helper::BaierUpperRewardBoundsComputer<ValueType> baier(ecElimRes.matrix, rewards, rew0StateProbs);
if (objective.upperResultBound) {
objective.upperResultBound = std::min(objective.upperResultBound.get(), baier.computeUpperBound());
} else {
objective.upperResultBound = baier.computeUpperBound();
}
}
}
}
if (objective.upperResultBound) {
STORM_LOG_INFO("Computed upper result bound " << objective.upperResultBound.get() << " for objective " << *objective.formula << ".");
} else {
STORM_LOG_WARN("Could not compute upper result bound for objective " << *objective.formula);
}
}
}
template class SparseMultiObjectiveRewardAnalysis<storm::models::sparse::Mdp<double>>;
template class SparseMultiObjectiveRewardAnalysis<storm::models::sparse::MarkovAutomaton<double>>;
template class SparseMultiObjectiveRewardAnalysis<storm::models::sparse::Mdp<storm::RationalNumber>>;
template class SparseMultiObjectiveRewardAnalysis<storm::models::sparse::MarkovAutomaton<storm::RationalNumber>>;
}
}
}
}

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src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectiveRewardAnalysis.h
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#pragma once
#include <memory>
#include <string>
#include "storm/logic/Formulas.h"
#include "storm/storage/BitVector.h"
#include "storm/storage/SparseMatrix.h"
#include "storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessorResult.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
namespace preprocessing {
enum class RewardFinitenessType {
AllFinite, // The expected reward is finite for all objectives and all schedulers
ExistsParetoFinite, // There is a Pareto optimal scheduler yielding finite rewards for all objectives
Infinite // All Pareto optimal schedulers yield infinite reward for at least one objective
};
/*
* This class performs some analysis task regarding the occurring expected reward objectives
*/
template <class SparseModelType>
class SparseMultiObjectiveRewardAnalysis {
public:
typedef typename SparseModelType::ValueType ValueType;
typedef typename SparseModelType::RewardModelType RewardModelType;
struct ReturnType {
RewardFinitenessType rewardFinitenessType;
// The states of the preprocessed model for which...
storm::storage::BitVector reward0EStates; // ... there is a scheduler such that all expected reward objectives have value zero
storm::storage::BitVector reward0AStates; // ... all schedulers induce value 0 for all expected reward objectives
boost::optional<storm::storage::BitVector> rewardLessInfinityEStates; // ... there is a scheduler yielding finite reward for all expected reward objectives
};
/*!
* Analyzes the reward objectives of the multi objective query
* @param preprocessorResult The result from preprocessing. Must only contain expected total reward or cumulative reward objectives.
*/
static ReturnType analyze(storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult);
/*!
* Tries to finds an upper bound for the expected reward of the objective (assuming it considers an expected total reward objective)
*/
static void computeUpperResultBound(SparseModelType const& model, storm::modelchecker::multiobjective::Objective<ValueType>& objective, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
private:
/*!
* Computes the set of states that have zero expected reward w.r.t. all expected total/cumulative reward objectives
*/
static void setReward0States(ReturnType& result, storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Checks whether the occurring expected rewards are finite and sets the RewardFinitenessType accordingly
* Returns the set of states for which a scheduler exists that induces finite reward for all objectives
*/
static void checkRewardFiniteness(ReturnType& result, storm::modelchecker::multiobjective::preprocessing::SparseMultiObjectivePreprocessorResult<SparseModelType> const& preprocessorResult, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
};
}
}
}
}
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