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Towards refactoring multi-objective preprocessing

tempestpy_adaptions
TimQu 8 years ago
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ee54c6cdac
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  1. 64
      src/storm/modelchecker/multiobjective/Objective.h
  2. 448
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
  3. 84
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h
  4. 73
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorReturnType.h
  5. 131
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorTask.h

64
src/storm/modelchecker/multiobjective/Objective.h
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#pragma once
#include <boost/optional.hpp>
#include "storm/logic/Formula.h"
#include "storm/logic/Bound.h"
#include "storm/logic/TimeBound.h"
#include "storm/solver/OptimizationDirection.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
template <typename ValueType>
struct Objective {
// the original input formula
std::shared_ptr<storm::logic::Formula const> originalFormula;
// the name of the considered reward model in the preprocessedModel
boost::optional<std::string> rewardModelName;
// True iff the complementary event is considered.
// E.g. if we consider P<1-t [F !"safe"] instead of P>=t [ G "safe"]
bool considersComplementaryEvent;
// The probability/reward threshold for the preprocessed model (if originalFormula specifies one).
boost::optional<storm::logic::Bound> bound;
// The optimization direction for the preprocessed model
// if originalFormula does ot specifies one, the direction is derived from the bound.
storm::solver::OptimizationDirection optimizationDirection;
// Lower and upper time/step bouds
boost::optional<storm::logic::TimeBound> lowerTimeBound, upperTimeBound;
void printToStream(std::ostream& out) const {
out << originalFormula->toString();
out << " \t";
out << "direction: ";
out << optimizationDirection;
out << " \t";
out << "intern bound: ";
if (bound){
out << bound;
} else {
out << " -none- ";
}
out << " \t";
out << "time bounds: ";
if (lowerTimeBound && upperTimeBound) {
out << (lowerTimeBound->isStrict() ? "(" : "[") << lowerTimeBound->getBound() << "," << upperTimeBound->getBound() << (upperTimeBound->isStrict() ? ")" : "]");
} else if (lowerTimeBound) {
out << (lowerTimeBound->isStrict() ? ">" : ">=") << lowerTimeBound->getBound();
} else if (upperTimeBound) {
out << (upperTimeBound->isStrict() ? "<" : "<=") << upperTimeBound->getBound();
} else {
out << " -none- ";
}
out << " \t";
out << "intern reward model: " << *rewardModelName;
}
};
}
}
}

448
src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
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#include "storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h"
#include <algorithm>
#include <storm/transformer/GoalStateMerger.h>
#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/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/SubsystemBuilder.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/utility/endComponents.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);
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");
}
}
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();
bool endComponentAnalysisRequired = false;
for (auto& task : data.tasks) {
endComponentAnalysisRequired = endComponentAnalysisRequired || task->requiresEndComponentAnalysis();
}
if (endComponentAnalysisRequired) {
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "End component analysis required but currently not implemented.");
}
for (auto& task : data.tasks) {
task->perform(*preprocessedModel);
}
ReturnType result(originalFormula, originalModel);
for (auto& obj : data.objectives) {
result.objectives.push_back(std::move(*obj));
}
result.preprocessedModel = std::move(preprocessedModel);
result.queryType = ReturnType::QueryType::Achievability;
auto minMaxNonZeroRewardStates = getStatesWithNonZeroRewardMinMax(result, backwardTransitions);
auto finiteRewardStates = ensureRewardFiniteness(result, data.finiteRewardCheckObjectives, minMaxNonZeroRewardStates.first, backwardTransitions);
std::set<std::string> relevantRewardModels;
for (auto const& obj : result.objectives) {
relevantRewardModels.insert(*obj.rewardModelName);
}
// Build a subsystem that discards states that yield infinite reward for all schedulers.
// We can also merge the states that will have reward zero anyway.
storm::storage::BitVector zeroRewardStates = ~minMaxNonZeroRewardStates.second;
storm::transformer::GoalStateMerger<SparseModelType> merger(*result.preprocessedModel);
typename storm::transformer::GoalStateMerger<SparseModelType>::ReturnType mergerResult = merger.mergeTargetAndSinkStates(finiteRewardStates, zeroRewardStates, storm::storage::BitVector(finiteRewardStates.size(), false), std::vector<std::string>(relevantRewardModels.begin(), relevantRewardModels.end()));
result.preprocessedModel = mergerResult.model;
result.possibleBottomStates = (~minMaxNonZeroRewardStates.first) % finiteRewardStates;
if (mergerResult.targetState) {
storm::storage::BitVector targetStateAsVector(result.preprocessedModel->getNumberOfStates(), false);
targetStateAsVector.set(*mergerResult.targetState, true);
result.possibleECChoices = storm::utility::graph::performProb0E(*result.preprocessedModel, result.preprocessedModel->getBackwardTransitions(), storm::storage::BitVector(targetStateAsVector.size(), true), targetStateAsVector);
result.possibleECChoices.set(result.preprocessedModel->getTransitionMatrix().getRowGroupIndices()[*mergerResult.targetState], true);
// There is an additional state in the result
result.possibleBottomStates.resize(result.possibleBottomStates.size() + 1, true);
}
assert(result.possibleBottomStates.size() == result.preprocessedModel->getNumberOfStates());
return result;
}
template <typename SparseModelType>
SparseMultiObjectivePreprocessor<SparseModelType>::PreprocessorData::PreprocessorData(SparseModelType const& model) : originalModel(model) {
storm::storage::MemoryStructureBuilder memoryBuilder(1);
memoryBuilder.setTransition(0,0, storm::logic::Formula::getTrueFormula());
memory = std::make_shared<storm::storage::MemoryStructure>(memoryBuilder.build());
// 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();
objective.considersComplementaryEvent = false;
if (formula.hasBound()) {
STORM_LOG_THROW(!formula.getBound().threshold.containsVariables(), storm::exceptions::InvalidPropertyException, "The formula " << formula << "considers a non-constant threshold");
objective.bound = formula.getBound();
if (storm::logic::isLowerBound(formula.getBound().comparisonType)) {
objective.optimizationDirection = storm::solver::OptimizationDirection::Maximize;
} else {
objective.optimizationDirection = storm::solver::OptimizationDirection::Minimize;
}
STORM_LOG_WARN_COND(!formula.hasOptimalityType() || formula.getOptimalityType() == objective.optimizationDirection, "Optimization direction of formula " << formula << " ignored as the formula also specifies a threshold.");
} else if (formula.hasOptimalityType()){
objective.optimizationDirection = formula.getOptimalityType();
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Current objective " << formula << " does not specify whether to minimize or maximize");
}
if (formula.isProbabilityOperatorFormula()){
preprocessProbabilityOperatorFormula(formula.asProbabilityOperatorFormula(), data);
} else if (formula.isRewardOperatorFormula()){
preprocessRewardOperatorFormula(formula.asRewardOperatorFormula(), data);
} else if (formula.isTimeOperatorFormula()){
preprocessTimeOperatorFormula(formula.asTimeOperatorFormula(), data);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "Could not preprocess the objective " << formula << " because it is not supported");
}
// Invert the bound and optimization direction (if necessary)
if (objective.considersComplementaryEvent) {
if (objective.bound) {
objective.bound->threshold = objective.bound->threshold.getManager().rational(storm::utility::one<storm::RationalNumber>()) - objective.bound->threshold;
objective.bound->comparisonType = storm::logic::invert(objective.bound->comparisonType);
}
objective.optimizationDirection = storm::solver::invert(objective.optimizationDirection);
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, PreprocessorData& data) {
if (formula.getSubformula().isUntilFormula()){
preprocessUntilFormula(formula.getSubformula().asUntilFormula(), data);
} else if (formula.getSubformula().isBoundedUntilFormula()){
preprocessBoundedUntilFormula(formula.getSubformula().asBoundedUntilFormula(), data);
} else if (formula.getSubformula().isGloballyFormula()){
preprocessGloballyFormula(formula.getSubformula().asGloballyFormula(), data);
} else if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), 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, 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;
}
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), data, rewardModelName);
} else if (formula.getSubformula().isCumulativeRewardFormula()) {
preprocessCumulativeRewardFormula(formula.getSubformula().asCumulativeRewardFormula(), data, rewardModelName);
} else if (formula.getSubformula().isTotalRewardFormula()) {
preprocessTotalRewardFormula(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, 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.");
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), 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, PreprocessorData& data) {
// Check if the formula is already satisfied in the initial state because then the transformation to expected rewards will fail.
if (!data.objectives.back()->lowerTimeBound) {
storm::modelchecker::SparsePropositionalModelChecker<SparseModelType> mc(data.originalModel);
if (!(data.originalModel.getInitialStates() & mc.check(formula.getRightSubformula())->asExplicitQualitativeCheckResult().getTruthValuesVector()).empty()) {
// TODO: Handle this case more properly
STORM_LOG_THROW(false, 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 builder(2);
std::string relevantStatesLabel = data.memoryLabelPrefix + "_obj" + std::to_string(data.objectives.size()) + "_relevant";
builder.setLabel(0, relevantStatesLabel);
auto negatedLeftSubFormula = std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, formula.getLeftSubformula().asSharedPointer());
auto targetFormula = std::make_shared<storm::logic::BinaryBooleanStateFormula>(storm::logic::BinaryBooleanStateFormula::OperatorType::Or, negatedLeftSubFormula, formula.getRightSubformula().asSharedPointer());
builder.setTransition(0, 0, std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, targetFormula));
builder.setTransition(0, 1, targetFormula);
builder.setTransition(1, 1, storm::logic::Formula::getTrueFormula());
storm::storage::MemoryStructure objectiveMemory = builder.build();
data.memory = std::make_shared<storm::storage::MemoryStructure>(data.memory->product(objectiveMemory));
data.objectives.back()->rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
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, PreprocessorData& data) {
STORM_LOG_THROW(!data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton) || !formula.isStepBounded(), storm::exceptions::InvalidPropertyException, "Multi-objective model checking currently does not support STEP-bounded properties for Markov automata.");
if (formula.hasLowerBound()) {
STORM_LOG_THROW(!formula.getLowerBound().containsVariables(), storm::exceptions::InvalidPropertyException, "The lower time bound for the formula " << formula << " still contains variables");
if (!storm::utility::isZero(formula.getLowerBound<double>()) || formula.isLowerBoundStrict()) {
data.objectives.back()->lowerTimeBound = storm::logic::TimeBound(formula.isLowerBoundStrict(), formula.getLowerBound());
}
}
if (formula.hasUpperBound()) {
STORM_LOG_THROW(!formula.getUpperBound().containsVariables(), storm::exceptions::InvalidPropertyException, "The Upper time bound for the formula " << formula << " still contains variables");
if (!storm::utility::isInfinity(formula.getUpperBound<double>())) {
data.objectives.back()->upperTimeBound = storm::logic::TimeBound(formula.isUpperBoundStrict(), formula.getUpperBound());
}
}
preprocessUntilFormula(storm::logic::UntilFormula(formula.getLeftSubformula().asSharedPointer(), formula.getRightSubformula().asSharedPointer()), data);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, PreprocessorData& data) {
// The formula will be transformed to an until formula for the complementary event.
data.objectives.back()->considersComplementaryEvent = true;
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), data);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
if (formula.isReachabilityProbabilityFormula()){
preprocessUntilFormula(storm::logic::UntilFormula(storm::logic::Formula::getTrueFormula(), formula.getSubformula().asSharedPointer()), data);
return;
}
// Create a memory structure that stores whether a target state has already been reached
storm::storage::MemoryStructureBuilder builder(2);
// 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, std::make_shared<storm::logic::UnaryBooleanStateFormula>(storm::logic::UnaryBooleanStateFormula::OperatorType::Not, formula.getSubformula().asSharedPointer()));
builder.setTransition(0, 1, formula.getSubformula().asSharedPointer());
builder.setTransition(1, 1, std::make_shared<storm::logic::BooleanLiteralFormula>(true));
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);
data.objectives.back()->rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
data.finiteRewardCheckObjectives.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()));
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1);
} else if (formula.isReachabilityTimeFormula() && data.originalModel.isOfType(storm::models::ModelType::MarkovAutomaton)) {
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1);
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, 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.");
STORM_LOG_THROW(!formula.getBound().containsVariables(), storm::exceptions::InvalidPropertyException, "The time bound for the formula " << formula << " still contains variables");
if (!storm::utility::isInfinity(formula.getBound<double>())) {
data.objectives.back()->upperTimeBound = storm::logic::TimeBound(formula.isBoundStrict(), formula.getBound());
}
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTotalRewardFormula(PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
assert(optionalRewardModelName.is_initialized());
data.objectives.back()->rewardModelName = *optionalRewardModelName;
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
}
template<typename SparseModelType>
std::pair<storm::storage::BitVector, storm::storage::BitVector> SparseMultiObjectivePreprocessor<SparseModelType>::getStatesWithNonZeroRewardMinMax(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) {
auto const& rewModel = result.preprocessedModel->getRewardModel(*obj.rewardModelName);
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 from which the minimal/maximal expected reward is always non-zero
storm::storage::BitVector minStates = storm::utility::graph::performProbGreater0A(transitions, groupIndices, backwardTransitions, allStates, statesWithRewardForAllChoices, false, 0, zeroRewardChoices);
storm::storage::BitVector maxStates = storm::utility::graph::performProbGreater0E(backwardTransitions, allStates, statesWithRewardForOneChoice);
STORM_LOG_ASSERT(minStates.isSubsetOf(maxStates), "The computed set of states with minimal non-zero expected rewards is not a subset of the states with maximal non-zero rewards.");
return std::make_pair(std::move(minStates), std::move(maxStates));
}
template<typename SparseModelType>
storm::storage::BitVector SparseMultiObjectivePreprocessor<SparseModelType>::ensureRewardFiniteness(ReturnType& result, storm::storage::BitVector const& finiteRewardCheckObjectives, storm::storage::BitVector const& nonZeroRewardMin, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {
auto const& transitions = result.preprocessedModel->getTransitionMatrix();
std::vector<uint_fast64_t> const& groupIndices = transitions.getRowGroupIndices();
storm::storage::BitVector maxRewardsToCheck(result.preprocessedModel->getNumberOfChoices(), true);
bool hasMinRewardToCheck;
for (auto const& objIndex : finiteRewardCheckObjectives) {
auto const& rewModel = result.preprocessedModel->getRewardModel(result.objectives[objIndex].rewardModelName.get());
if (storm::solver::minimize(result.objectives[objIndex].optimizationDirection)) {
hasMinRewardToCheck = true;
} else {
maxRewardsToCheck &= rewModel.getChoicesWithZeroReward(transitions);
}
}
maxRewardsToCheck.complement();
// Assert reward finitiness for maximizing objectives under all schedulers
storm::storage::BitVector allStates(result.preprocessedModel->getNumberOfStates(), true);
if (storm::utility::endComponents::checkIfECWithChoiceExists(transitions, backwardTransitions, allStates, maxRewardsToCheck)) {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "At least one of the maximizing objectives induces infinite expected reward (or time). This is not supported");
}
// Assert that there is one scheduler under which all rewards are finite.
// This only has to be done if there are minimizing expected rewards that potentially can be infinite
storm::storage::BitVector finiteRewardStates;
if (hasMinRewardToCheck) {
finiteRewardStates = storm::utility::graph::performProb1E(transitions, groupIndices, backwardTransitions, allStates, ~nonZeroRewardMin);
if ((finiteRewardStates & result.preprocessedModel->getInitialStates()).empty()) {
// There is no scheduler that induces finite reward for the initial state
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "For every scheduler, at least one objective gets infinite reward.");
}
} else {
finiteRewardStates = allStates;
}
return finiteRewardStates;
}
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/SparseMultiObjectivePreprocessorReturnType.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 SparseMultiObjectivePreprocessorReturnType<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<CbObjective<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;
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 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, PreprocessorData& data);
static void preprocessRewardOperatorFormula(storm::logic::RewardOperatorFormula const& formula, PreprocessorData& data);
static void preprocessTimeOperatorFormula(storm::logic::TimeOperatorFormula const& formula, PreprocessorData& data);
static void preprocessUntilFormula(storm::logic::UntilFormula const& formula, PreprocessorData& data);
static void preprocessBoundedUntilFormula(storm::logic::BoundedUntilFormula const& formula, PreprocessorData& data);
static void preprocessGloballyFormula(storm::logic::GloballyFormula const& formula, PreprocessorData& data);
static void preprocessEventuallyFormula(storm::logic::EventuallyFormula const& formula, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessCumulativeRewardFormula(storm::logic::CumulativeRewardFormula const& formula, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessTotalRewardFormula(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.
/*!
* Computes the set of states that have a non-zero reward w.r.t. all objectives, assuming that the objectives are all minimizing and maximizing, respectively.
*/
static std::pair<storm::storage::BitVector, storm::storage::BitVector> getStatesWithNonZeroRewardMinMax(ReturnType& result, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
/*!
* Checks whether the occurring expected rewards are finite. If not, the input is rejected.
* Returns the set of states for which a scheduler exists that induces finite reward for all objectives
*/
static storm::storage::BitVector ensureRewardFiniteness(ReturnType& result, storm::storage::BitVector const& finiteRewardCheckObjectives, storm::storage::BitVector const& nonZeroRewardMin, storm::storage::SparseMatrix<ValueType> const& backwardTransitions);
};
}
}
}

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src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorReturnType.h
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#pragma once
#include <vector>
#include <memory>
#include <boost/optional.hpp>
#include "storm/logic/Formulas.h"
#include "storm/modelchecker/multiobjective/constraintbased/CbObjective.h"
#include "storm/exceptions/UnexpectedException.h"
namespace storm {
namespace modelchecker {
namespace multiobjective {
template <class SparseModelType>
struct SparseCbPreprocessorReturnType {
enum class QueryType { Achievability };
storm::logic::MultiObjectiveFormula const& originalFormula;
SparseModelType const& originalModel;
std::shared_ptr<SparseModelType> preprocessedModel;
QueryType queryType;
// The (preprocessed) objectives
std::vector<CbObjective<typename SparseModelType::ValueType>> objectives;
// The states for which there is a scheduler such that all objectives have value zero
storm::storage::BitVector possibleBottomStates;
// Overapproximation of the set of choices that are part of an end component.
storm::storage::BitVector possibleECChoices;
SparseCbPreprocessorReturnType(storm::logic::MultiObjectiveFormula const& originalFormula, SparseModelType const& originalModel) : originalFormula(originalFormula), originalModel(originalModel) {
// Intentionally left empty
}
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, SparseCbPreprocessorReturnType<SparseModelType> const& ret) {
ret.printToStream(out);
return out;
}
};
}
}
}

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src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorTask.h
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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 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->rewardModelName.is_initialized(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->rewardModelName.get(), 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->rewardModelName.is_initialized(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->rewardModelName.get(), 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->rewardModelName.is_initialized(), "No reward model name has been specified");
preprocessedModel.addRewardModel(this->objective->rewardModelName.get(), typename SparseModelType::RewardModelType(std::move(timeRewards)));
}
private:
std::shared_ptr<storm::logic::Formula const> relevantStateFormula;
};
}
}
}
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