sp
/
tempest
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Implemented stripping away of reward accumulations in multi-objective preprocessing.

tempestpy_adaptions
TimQu 4 years ago
parent
f532f6461b
commit
e898391e44
2 changed files with 61 additions and 20 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 77
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.cpp
  2. 4
      src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.h

77
src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.cpp
View File

@ -348,9 +348,9 @@ namespace storm {
} else if (formula.getSubformula().isCumulativeRewardFormula()) {
preprocessCumulativeRewardFormula(formula.getSubformula().asCumulativeRewardFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isTotalRewardFormula()) {
preprocessTotalRewardFormula(opInfo, data, rewardModelName);
preprocessTotalRewardFormula(formula.getSubformula().asTotalRewardFormula(), opInfo, data, rewardModelName);
} else if (formula.getSubformula().isLongRunAverageRewardFormula()) {
preprocessLongRunAverageRewardFormula(opInfo, data, rewardModelName);
preprocessLongRunAverageRewardFormula(formula.getSubformula().asLongRunAverageRewardFormula(), opInfo, data, rewardModelName);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidPropertyException, "The subformula of " << formula << " is not supported.");
}
@ -498,7 +498,8 @@ namespace storm {
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());
auto objectiveRewards = storm::utility::createFilteredRewardModel(data.model->getRewardModel(optionalRewardModelName.get()), data.model->isDiscreteTimeModel(), formula).extract();
// get rid of potential transition rewards
objectiveRewards.reduceToStateBasedRewards(data.model->getTransitionMatrix(), false);
if (objectiveRewards.hasStateRewards()) {
storm::utility::vector::setVectorValues(objectiveRewards.getStateRewardVector(), reachableFromGoal, storm::utility::zero<typename SparseModelType::ValueType>());
@ -540,7 +541,7 @@ namespace storm {
} else {
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)) {
} else if (formula.isReachabilityTimeFormula()) {
// Reduce to reachability rewards so that time formulas do not have to be treated seperately later.
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
std::shared_ptr<storm::logic::Formula const> newSubformula = formula.getSubformula().asSharedPointer();
@ -551,8 +552,14 @@ namespace storm {
}
auto newFormula = std::make_shared<storm::logic::EventuallyFormula>(newSubformula, storm::logic::FormulaContext::Reward);
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(newFormula, rewardModelName, opInfo);
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(), storm::utility::one<typename SparseModelType::ValueType>());
std::vector<typename SparseModelType::ValueType> timeRewards;
if (data.model->isOfType(storm::models::ModelType::MarkovAutomaton)) {
timeRewards.assign(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(), storm::utility::one<typename SparseModelType::ValueType>());
} else {
timeRewards.assign(data.model->getNumberOfStates(), 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.");
@ -564,34 +571,68 @@ namespace storm {
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.");
std::string rewardModelName = optionalRewardModelName.get();
// Strip away potential RewardAccumulations in the formula itself but also in reward bounds
auto filteredRewards = storm::utility::createFilteredRewardModel(data.model->getRewardModel(rewardModelName), data.model->isDiscreteTimeModel(), formula);
if (filteredRewards.isDifferentFromUnfilteredModel()) {
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
data.model->addRewardModel(rewardModelName, std::move(filteredRewards.extract()));
}
auto cumulativeRewardFormula = std::make_shared<storm::logic::CumulativeRewardFormula>(formula);
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(cumulativeRewardFormula, *optionalRewardModelName, opInfo);
std::vector<storm::logic::TimeBoundReference> newTimeBoundReferences;
bool onlyRewardBounds = true;
for (uint64_t i = 0; i < cumulativeRewardFormula->getDimension(); ++i) {
if (!cumulativeRewardFormula->getTimeBoundReference(i).isRewardBound()) {
for (uint64_t i = 0; i < formula.getDimension(); ++i) {
auto oldTbr = formula.getTimeBoundReference(i);
if (oldTbr.isRewardBound()) {
onlyRewardBounds = false;
break;
if (oldTbr.hasRewardAccumulation()) {
auto filteredBoundRewards = storm::utility::createFilteredRewardModel(data.model->getRewardModel(oldTbr.getRewardName()), oldTbr.getRewardAccumulation(), data.model->isDiscreteTimeModel());
if (filteredBoundRewards.isDifferentFromUnfilteredModel()) {
std::string freshRewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size()) + std::string("_" + std::to_string(i));
data.model->addRewardModel(freshRewardModelName, std::move(filteredBoundRewards.extract()));
newTimeBoundReferences.emplace_back(freshRewardModelName);
} else {
// Strip away the reward accumulation
newTimeBoundReferences.emplace_back(oldTbr.getRewardName());
}
} else {
newTimeBoundReferences.push_back(oldTbr);
}
} else {
newTimeBoundReferences.push_back(oldTbr);
}
}
auto newFormula = std::make_shared<storm::logic::CumulativeRewardFormula>(formula.getBounds(), newTimeBoundReferences);
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(newFormula, rewardModelName, opInfo);
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) {
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessTotalRewardFormula(storm::logic::TotalRewardFormula const& formula, 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);
std::string rewardModelName = optionalRewardModelName.get();
auto filteredRewards = storm::utility::createFilteredRewardModel(data.model->getRewardModel(rewardModelName), data.model->isDiscreteTimeModel(), formula);
if (filteredRewards.isDifferentFromUnfilteredModel()) {
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
data.model->addRewardModel(rewardModelName, filteredRewards.extract());
}
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(formula.stripRewardAccumulation(), rewardModelName, opInfo);
data.finiteRewardCheckObjectives.set(data.objectives.size() - 1, true);
}
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessLongRunAverageRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
auto longRunAverageRewardFormula = std::make_shared<storm::logic::LongRunAverageRewardFormula>();
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(longRunAverageRewardFormula, *optionalRewardModelName, opInfo);
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessLongRunAverageRewardFormula(storm::logic::LongRunAverageRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName) {
std::string rewardModelName = optionalRewardModelName.get();
auto filteredRewards = storm::utility::createFilteredRewardModel(data.model->getRewardModel(rewardModelName), data.model->isDiscreteTimeModel(), formula);
if (filteredRewards.isDifferentFromUnfilteredModel()) {
std::string rewardModelName = data.rewardModelNamePrefix + std::to_string(data.objectives.size());
data.model->addRewardModel(rewardModelName, std::move(filteredRewards.extract()));
}
data.objectives.back()->formula = std::make_shared<storm::logic::RewardOperatorFormula>(formula.stripRewardAccumulation(), rewardModelName, opInfo);
}
template<typename SparseModelType>

4
src/storm/modelchecker/multiobjective/preprocessing/SparseMultiObjectivePreprocessor.h
View File

@ -75,8 +75,8 @@ namespace storm {
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.
static void preprocessLongRunAverageRewardFormula(storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none); // The long run average reward formula itself does not need to be provided as it is unique.
static void preprocessTotalRewardFormula(storm::logic::TotalRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
static void preprocessLongRunAverageRewardFormula(storm::logic::LongRunAverageRewardFormula const& formula, storm::logic::OperatorInformation const& opInfo, PreprocessorData& data, boost::optional<std::string> const& optionalRewardModelName = boost::none);
/*!
* Builds the result from preprocessing

Write Preview
Loading…
Cancel
Save