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Improved and fixed multiObjectivePreprocessor

tempestpy_adaptions
TimQu 8 years ago
parent
6598ade4ac
commit
cdb923403f
4 changed files with 110 additions and 40 deletions
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  1. 22
      src/storm/modelchecker/multiobjective/Objective.h
  2. 114
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
  3. 11
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h
  4. 3
      src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorReturnType.h

22
src/storm/modelchecker/multiobjective/Objective.h
View File

@ -31,6 +31,8 @@ namespace storm {
// Lower and upper time/step bouds
boost::optional<storm::logic::TimeBound> lowerTimeBound, upperTimeBound;
boost::optional<ValueType> lowerResultBound, upperResultBound;
void printToStream(std::ostream& out) const {
out << originalFormula->toString();
out << " \t";
@ -39,7 +41,7 @@ namespace storm {
out << " \t";
out << "intern bound: ";
if (bound){
out << bound;
out << *bound;
} else {
out << " -none- ";
}
@ -55,7 +57,23 @@ namespace storm {
out << " -none- ";
}
out << " \t";
out << "intern reward model: " << *rewardModelName;
out << "intern reward model: ";
if (rewardModelName) {
out << *rewardModelName;
} else {
out << " -none- ";
}
out << " \t";
out << "result bounds: ";
if (lowerResultBound && upperResultBound) {
out << "[" << *lowerResultBound << ", " << *upperResultBound << "]";
} else if (lowerResultBound) {
out << ">=" << *lowerResultBound;
} else if (upperResultBound) {
out << "<=" << *upperResultBound;
} else {
out << " -none- ";
}
}
};
}

114
src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.cpp
View File

@ -44,16 +44,19 @@ namespace storm {
}
}
// 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.");
}
@ -61,41 +64,8 @@ namespace storm {
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;
// Build the actual result
return buildResult(originalModel, originalFormula, data, preprocessedModel, backwardTransitions);
}
template <typename SparseModelType>
@ -139,8 +109,6 @@ namespace storm {
break;
}
}
}
template<typename SparseModelType>
@ -189,6 +157,10 @@ namespace storm {
template<typename SparseModelType>
void SparseMultiObjectivePreprocessor<SparseModelType>::preprocessProbabilityOperatorFormula(storm::logic::ProbabilityOperatorFormula const& formula, 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(), data);
} else if (formula.getSubformula().isBoundedUntilFormula()){
@ -217,6 +189,8 @@ namespace storm {
rewardModelName = data.originalModel.getRewardModels().begin()->first;
}
data.objectives.back()->lowerResultBound = storm::utility::zero<ValueType>();
if (formula.getSubformula().isEventuallyFormula()){
preprocessEventuallyFormula(formula.getSubformula().asEventuallyFormula(), data, rewardModelName);
} else if (formula.getSubformula().isCumulativeRewardFormula()) {
@ -233,6 +207,8 @@ namespace storm {
// 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(), data);
} else {
@ -344,6 +320,8 @@ namespace storm {
data.objectives.back()->upperTimeBound = storm::logic::TimeBound(formula.isBoundStrict(), formula.getBound());
}
assert(optionalRewardModelName.is_initialized());
data.objectives.back()->rewardModelName = *optionalRewardModelName;
}
@ -354,6 +332,68 @@ namespace storm {
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, 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);
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::storage::BitVector maybeStates = finiteRewardStates & ~zeroRewardStates;
storm::transformer::GoalStateMerger<SparseModelType> merger(*result.preprocessedModel);
typename storm::transformer::GoalStateMerger<SparseModelType>::ReturnType mergerResult = merger.mergeTargetAndSinkStates(maybeStates, zeroRewardStates, storm::storage::BitVector(maybeStates.size(), false), std::vector<std::string>(relevantRewardModels.begin(), relevantRewardModels.end()));
result.preprocessedModel = mergerResult.model;
result.possibleBottomStates = (~minMaxNonZeroRewardStates.first) % maybeStates;
if (mergerResult.targetState) {
storm::storage::BitVector targetStateAsVector(result.preprocessedModel->getNumberOfStates(), false);
targetStateAsVector.set(*mergerResult.targetState, true);
result.possibleECChoices = result.preprocessedModel->getTransitionMatrix().getRowIndicesOfRowGroups(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>
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.bound) {
++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>
std::pair<storm::storage::BitVector, storm::storage::BitVector> SparseMultiObjectivePreprocessor<SparseModelType>::getStatesWithNonZeroRewardMinMax(ReturnType& result, storm::storage::SparseMatrix<ValueType> const& backwardTransitions) {

11
src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessor.h
View File

@ -65,6 +65,17 @@ namespace storm {
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.
/*!
* 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 a non-zero reward w.r.t. all objectives, assuming that the objectives are all minimizing and maximizing, respectively.
*/

3
src/storm/modelchecker/multiobjective/SparseMultiObjectivePreprocessorReturnType.h
View File

@ -6,6 +6,7 @@
#include "storm/logic/Formulas.h"
#include "storm/modelchecker/multiobjective/Objective.h"
#include "storm/storage/BitVector.h"
#include "storm/exceptions/UnexpectedException.h"
@ -16,7 +17,7 @@ namespace storm {
template <class SparseModelType>
struct SparseMultiObjectivePreprocessorReturnType {
enum class QueryType { Achievability };
enum class QueryType { Achievability, Quantitative, Pareto };
storm::logic::MultiObjectiveFormula const& originalFormula;
SparseModelType const& originalModel;

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