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tempest/src/storm/modelchecker/prctl/helper/SparseMdpPrctlHelper.cpp

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#include "storm/modelchecker/prctl/helper/SparseMdpPrctlHelper.h"
#include <boost/container/flat_map.hpp>
#include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
#include "storm/modelchecker/hints/ExplicitModelCheckerHint.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
#include "storm/utility/graph.h"
#include "storm/storage/expressions/Variable.h"
#include "storm/storage/expressions/Expression.h"
#include "storm/storage/Scheduler.h"
#include "storm/solver/MinMaxLinearEquationSolver.h"
#include "storm/solver/LpSolver.h"
#include "storm/settings/SettingsManager.h"
#include "storm/settings/modules/MinMaxEquationSolverSettings.h"
#include "storm/exceptions/InvalidStateException.h"
#include "storm/exceptions/InvalidPropertyException.h"
#include "storm/exceptions/InvalidSettingsException.h"
#include "storm/exceptions/IllegalFunctionCallException.h"
#include "storm/exceptions/IllegalArgumentException.h"
#include "storm/exceptions/UncheckedRequirementException.h"
#include "storm/exceptions/NotSupportedException.h"
namespace storm {
namespace modelchecker {
namespace helper {
template<typename ValueType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeBoundedUntilProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, uint_fast64_t stepBound, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
std::vector<ValueType> result(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
// Determine the states that have 0 probability of reaching the target states.
storm::storage::BitVector maybeStates;
if (hint.isExplicitModelCheckerHint() && hint.template asExplicitModelCheckerHint<ValueType>().getComputeOnlyMaybeStates()) {
maybeStates = hint.template asExplicitModelCheckerHint<ValueType>().getMaybeStates();
} else {
if (dir == OptimizationDirection::Minimize) {
maybeStates = storm::utility::graph::performProbGreater0A(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, phiStates, psiStates, true, stepBound);
} else {
maybeStates = storm::utility::graph::performProbGreater0E(backwardTransitions, phiStates, psiStates, true, stepBound);
}
maybeStates &= ~psiStates;
STORM_LOG_INFO("Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
}
if (!maybeStates.empty()) {
// We can eliminate the rows and columns from the original transition probability matrix that have probability 0.
storm::storage::SparseMatrix<ValueType> submatrix = transitionMatrix.getSubmatrix(true, maybeStates, maybeStates, false);
std::vector<ValueType> b = transitionMatrix.getConstrainedRowGroupSumVector(maybeStates, psiStates);
// Create the vector with which to multiply.
std::vector<ValueType> subresult(maybeStates.getNumberOfSetBits());
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(std::move(submatrix));
solver->repeatedMultiply(dir, subresult, &b, stepBound);
// Set the values of the resulting vector accordingly.
storm::utility::vector::setVectorValues(result, maybeStates, subresult);
}
storm::utility::vector::setVectorValues(result, psiStates, storm::utility::one<ValueType>());
return result;
}
template<typename ValueType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeNextProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& nextStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Create the vector with which to multiply and initialize it correctly.
std::vector<ValueType> result(transitionMatrix.getRowGroupCount());
storm::utility::vector::setVectorValues(result, nextStates, storm::utility::one<ValueType>());
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(transitionMatrix);
solver->repeatedMultiply(dir, result, nullptr, 1);
return result;
}
template<typename ValueType>
std::vector<uint_fast64_t> computeValidSchedulerHint(storm::solver::EquationSystemType const& type, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& maybeStates, storm::storage::BitVector const& filterStates, storm::storage::BitVector const& targetStates) {
storm::storage::Scheduler<ValueType> validScheduler(maybeStates.size());
if (type == storm::solver::EquationSystemType::UntilProbabilities) {
storm::utility::graph::computeSchedulerProbGreater0E(transitionMatrix, backwardTransitions, filterStates, targetStates, validScheduler, boost::none);
} else if (type == storm::solver::EquationSystemType::ReachabilityRewards) {
storm::utility::graph::computeSchedulerProb1E(maybeStates | targetStates, transitionMatrix, backwardTransitions, filterStates, targetStates, validScheduler);
} else {
STORM_LOG_ASSERT(false, "Unexpected equation system type.");
}
// Extract the relevant parts of the scheduler for the solver.
std::vector<uint_fast64_t> schedulerHint(maybeStates.getNumberOfSetBits());
auto maybeIt = maybeStates.begin();
for (auto& choice : schedulerHint) {
choice = validScheduler.getChoice(*maybeIt).getDeterministicChoice();
++maybeIt;
}
return schedulerHint;
}
template<typename ValueType>
struct SparseMdpHintType {
SparseMdpHintType() : eliminateEndComponents(false) {
// Intentionally left empty.
}
bool hasSchedulerHint() const {
return static_cast<bool>(schedulerHint);
}
bool hasValueHint() const {
return static_cast<bool>(valueHint);
}
bool hasLowerResultBound() const {
return static_cast<bool>(lowerResultBound);
}
ValueType const& getLowerResultBound() const {
return lowerResultBound.get();
}
bool hasUpperResultBound() const {
return static_cast<bool>(upperResultBound);
}
ValueType const& getUpperResultBound() const {
return upperResultBound.get();
}
std::vector<uint64_t>& getSchedulerHint() {
return schedulerHint.get();
}
std::vector<ValueType>& getValueHint() {
return valueHint.get();
}
bool getEliminateEndComponents() const {
return eliminateEndComponents;
}
boost::optional<std::vector<uint64_t>> schedulerHint;
boost::optional<std::vector<ValueType>> valueHint;
boost::optional<ValueType> lowerResultBound;
boost::optional<ValueType> upperResultBound;
bool eliminateEndComponents;
};
template<typename ValueType>
void extractValueAndSchedulerHint(SparseMdpHintType<ValueType>& hintStorage, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& maybeStates, boost::optional<storm::storage::BitVector> const& selectedChoices, ModelCheckerHint const& hint, bool skipECWithinMaybeStatesCheck) {
// Deal with scheduler hint.
if (hint.isExplicitModelCheckerHint() && hint.template asExplicitModelCheckerHint<ValueType>().hasSchedulerHint()) {
if (hintStorage.hasSchedulerHint()) {
STORM_LOG_WARN("A scheduler hint was provided, but the solver requires a specific one. The provided scheduler hint will be ignored.");
} else {
auto const& schedulerHint = hint.template asExplicitModelCheckerHint<ValueType>().getSchedulerHint();
std::vector<uint64_t> hintChoices;
// The scheduler hint is only applicable if it induces no BSCC consisting of maybe states.
bool hintApplicable;
if (!skipECWithinMaybeStatesCheck) {
hintChoices.reserve(maybeStates.size());
for (uint_fast64_t state = 0; state < maybeStates.size(); ++state) {
hintChoices.push_back(schedulerHint.getChoice(state).getDeterministicChoice());
}
hintApplicable = storm::utility::graph::performProb1(transitionMatrix.transposeSelectedRowsFromRowGroups(hintChoices), maybeStates, ~maybeStates).full();
} else {
hintApplicable = true;
}
if (hintApplicable) {
// Compute the hint w.r.t. the given subsystem.
hintChoices.clear();
hintChoices.reserve(maybeStates.getNumberOfSetBits());
for (auto const& state : maybeStates) {
uint_fast64_t hintChoice = schedulerHint.getChoice(state).getDeterministicChoice();
if (selectedChoices) {
uint_fast64_t firstChoice = transitionMatrix.getRowGroupIndices()[state];
uint_fast64_t lastChoice = firstChoice + hintChoice;
hintChoice = 0;
for (uint_fast64_t choice = selectedChoices->getNextSetIndex(firstChoice); choice < lastChoice; choice = selectedChoices->getNextSetIndex(choice + 1)) {
++hintChoice;
}
}
hintChoices.push_back(hintChoice);
}
hintStorage.schedulerHint = std::move(hintChoices);
}
}
}
// Deal with solution value hint. Only applicable if there are no End Components consisting of maybe states.
if (hint.isExplicitModelCheckerHint() && hint.template asExplicitModelCheckerHint<ValueType>().hasResultHint() && (skipECWithinMaybeStatesCheck || hintStorage.hasSchedulerHint() || storm::utility::graph::performProb1A(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, maybeStates, ~maybeStates).full())) {
hintStorage.valueHint = storm::utility::vector::filterVector(hint.template asExplicitModelCheckerHint<ValueType>().getResultHint(), maybeStates);
}
}
template<typename ValueType>
SparseMdpHintType<ValueType> computeHints(storm::solver::EquationSystemType const& type, ModelCheckerHint const& hint, storm::OptimizationDirection const& dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& maybeStates, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& targetStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, boost::optional<storm::storage::BitVector> const& selectedChoices = boost::none) {
SparseMdpHintType<ValueType> result;
// Check for requirements of the solver.
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(type, dir);
if (!requirements.empty()) {
// If the hint tells us that there are no end-components, we can clear that requirement.
if (hint.isExplicitModelCheckerHint() && hint.asExplicitModelCheckerHint<ValueType>().getNoEndComponentsInMaybeStates()) {
requirements.clearNoEndComponents();
}
// If the solver still requires no end-components, we have to eliminate them later.
if (requirements.requiresNoEndComponents()) {
STORM_LOG_DEBUG("Scheduling EC elimination, because the solver requires it.");
result.eliminateEndComponents = true;
requirements.clearNoEndComponents();
}
// If the solver requires an initial scheduler, compute one now.
if (requirements.requires(storm::solver::MinMaxLinearEquationSolverRequirements::Element::ValidInitialScheduler)) {
STORM_LOG_DEBUG("Computing valid scheduler, because the solver requires it.");
result.schedulerHint = computeValidSchedulerHint(type, transitionMatrix, backwardTransitions, maybeStates, phiStates, targetStates);
requirements.clearValidInitialScheduler();
}
// Finally, we have information on the bounds depending on the problem type.
if (type == storm::solver::EquationSystemType::UntilProbabilities) {
requirements.clearBounds();
} else if (type == storm::solver::EquationSystemType::ReachabilityRewards) {
requirements.clearLowerBounds();
}
STORM_LOG_THROW(requirements.empty(), storm::exceptions::UncheckedRequirementException, "There are unchecked requirements of the solver.");
} else {
STORM_LOG_DEBUG("Solver has no requirements.");
}
// Only if there is no end component decomposition that we will need to do later, we use value and scheduler
// hints from the provided hint.
if (!result.eliminateEndComponents) {
bool skipEcWithinMaybeStatesCheck = dir == storm::OptimizationDirection::Minimize || (hint.isExplicitModelCheckerHint() && hint.asExplicitModelCheckerHint<ValueType>().getNoEndComponentsInMaybeStates());
extractValueAndSchedulerHint(result, transitionMatrix, backwardTransitions, maybeStates, selectedChoices, hint, skipEcWithinMaybeStatesCheck);
} else {
STORM_LOG_WARN_COND(hint.isEmpty(), "A non-empty hint was provided, but its information will be disregarded.");
}
// Only set bounds if we did not obtain them from the hint.
if (!result.hasLowerResultBound()) {
result.lowerResultBound = storm::utility::zero<ValueType>();
}
if (!result.hasUpperResultBound() && type == storm::solver::EquationSystemType::UntilProbabilities) {
result.upperResultBound = storm::utility::one<ValueType>();
}
return result;
}
template<typename ValueType>
struct MaybeStateResult {
MaybeStateResult(std::vector<ValueType>&& values) : values(std::move(values)) {
// Intentionally left empty.
}
bool hasScheduler() const {
return static_cast<bool>(scheduler);
}
std::vector<uint64_t> const& getScheduler() const {
return scheduler.get();
}
std::vector<ValueType> const& getValues() const {
return values;
}
std::vector<ValueType> values;
boost::optional<std::vector<uint64_t>> scheduler;
};
template<typename ValueType>
MaybeStateResult<ValueType> computeValuesForMaybeStates(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& submatrix, std::vector<ValueType> const& b, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, SparseMdpHintType<ValueType>& hint) {
// Set up the solver.
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = storm::solver::configureMinMaxLinearEquationSolver(goal, minMaxLinearEquationSolverFactory, submatrix);
solver->setRequirementsChecked();
if (hint.hasLowerResultBound()) {
solver->setLowerBound(hint.getLowerResultBound());
}
if (hint.hasUpperResultBound()) {
solver->setUpperBound(hint.getUpperResultBound());
}
if (hint.hasSchedulerHint()) {
solver->setInitialScheduler(std::move(hint.getSchedulerHint()));
}
solver->setTrackScheduler(produceScheduler);
// Initialize the solution vector.
std::vector<ValueType> x = hint.hasValueHint() ? std::move(hint.getValueHint()) : std::vector<ValueType>(submatrix.getRowGroupCount(), hint.hasLowerResultBound() ? hint.getLowerResultBound() : storm::utility::zero<ValueType>());
// Solve the corresponding system of equations.
solver->solveEquations(x, b);
// Create result.
MaybeStateResult<ValueType> result(std::move(x));
// If requested, return the requested scheduler.
if (produceScheduler) {
result.scheduler = std::move(solver->getSchedulerChoices());
}
return result;
}
struct QualitativeStateSetsUntilProbabilities {
storm::storage::BitVector maybeStates;
storm::storage::BitVector statesWithProbability0;
storm::storage::BitVector statesWithProbability1;
};
template<typename ValueType>
QualitativeStateSetsUntilProbabilities getQualitativeStateSetsUntilProbabilitiesFromHint(ModelCheckerHint const& hint) {
QualitativeStateSetsUntilProbabilities result;
result.maybeStates = hint.template asExplicitModelCheckerHint<ValueType>().getMaybeStates();
// Treat the states with probability zero/one.
std::vector<ValueType> const& resultsForNonMaybeStates = hint.template asExplicitModelCheckerHint<ValueType>().getResultHint();
result.statesWithProbability1 = storm::storage::BitVector(result.maybeStates.size());
result.statesWithProbability0 = storm::storage::BitVector(result.maybeStates.size());
storm::storage::BitVector nonMaybeStates = ~result.maybeStates;
for (auto const& state : nonMaybeStates) {
if (storm::utility::isOne(resultsForNonMaybeStates[state])) {
result.statesWithProbability1.set(state, true);
} else {
STORM_LOG_THROW(storm::utility::isZero(resultsForNonMaybeStates[state]), storm::exceptions::IllegalArgumentException, "Expected that the result hint specifies probabilities in {0,1} for non-maybe states");
result.statesWithProbability0.set(state, true);
}
}
return result;
}
template<typename ValueType>
QualitativeStateSetsUntilProbabilities computeQualitativeStateSetsUntilProbabilities(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
QualitativeStateSetsUntilProbabilities result;
// Get all states that have probability 0 and 1 of satisfying the until-formula.
std::pair<storm::storage::BitVector, storm::storage::BitVector> statesWithProbability01;
if (goal.minimize()) {
statesWithProbability01 = storm::utility::graph::performProb01Min(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, phiStates, psiStates);
} else {
statesWithProbability01 = storm::utility::graph::performProb01Max(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, phiStates, psiStates);
}
result.statesWithProbability0 = std::move(statesWithProbability01.first);
result.statesWithProbability1 = std::move(statesWithProbability01.second);
result.maybeStates = ~(result.statesWithProbability0 | result.statesWithProbability1);
STORM_LOG_INFO("Found " << result.statesWithProbability0.getNumberOfSetBits() << " 'no' states.");
STORM_LOG_INFO("Found " << result.statesWithProbability1.getNumberOfSetBits() << " 'yes' states.");
STORM_LOG_INFO("Found " << result.maybeStates.getNumberOfSetBits() << " 'maybe' states.");
return result;
}
template<typename ValueType>
QualitativeStateSetsUntilProbabilities getQualitativeStateSetsUntilProbabilities(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, ModelCheckerHint const& hint) {
if (hint.isExplicitModelCheckerHint() && hint.template asExplicitModelCheckerHint<ValueType>().getComputeOnlyMaybeStates()) {
return getQualitativeStateSetsUntilProbabilitiesFromHint<ValueType>(hint);
} else {
return computeQualitativeStateSetsUntilProbabilities(goal, transitionMatrix, backwardTransitions, phiStates, psiStates);
}
}
template<typename ValueType>
void extractSchedulerChoices(storm::storage::Scheduler<ValueType>& scheduler, std::vector<uint_fast64_t> const& subChoices, storm::storage::BitVector const& maybeStates) {
auto subChoiceIt = subChoices.begin();
for (auto maybeState : maybeStates) {
scheduler.setChoice(*subChoiceIt, maybeState);
++subChoiceIt;
}
assert(subChoiceIt == subChoices.end());
}
template<typename ValueType>
void extendScheduler(storm::storage::Scheduler<ValueType>& scheduler, storm::solver::SolveGoal const& goal, QualitativeStateSetsUntilProbabilities const& qualitativeStateSets, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
// Finally, if we need to produce a scheduler, we also need to figure out the parts of the scheduler for
// the states with probability 1 or 0 (depending on whether we maximize or minimize).
// We also need to define some arbitrary choice for the remaining states to obtain a fully defined scheduler.
if (goal.minimize()) {
storm::utility::graph::computeSchedulerProb0E(qualitativeStateSets.statesWithProbability0, transitionMatrix, scheduler);
for (auto const& prob1State : qualitativeStateSets.statesWithProbability1) {
scheduler.setChoice(0, prob1State);
}
} else {
storm::utility::graph::computeSchedulerProb1E(qualitativeStateSets.statesWithProbability1, transitionMatrix, backwardTransitions, phiStates, psiStates, scheduler);
for (auto const& prob0State : qualitativeStateSets.statesWithProbability0) {
scheduler.setChoice(0, prob0State);
}
}
}
template<typename ValueType>
void computeFixedPointSystemUntilProbabilities(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, QualitativeStateSetsUntilProbabilities const& qualitativeStateSets, storm::storage::SparseMatrix<ValueType>& submatrix, std::vector<ValueType>& b) {
// First, we can eliminate the rows and columns from the original transition probability matrix for states
// whose probabilities are already known.
submatrix = transitionMatrix.getSubmatrix(true, qualitativeStateSets.maybeStates, qualitativeStateSets.maybeStates, false);
// Prepare the right-hand side of the equation system. For entry i this corresponds to
// the accumulated probability of going from state i to some state that has probability 1.
b = transitionMatrix.getConstrainedRowGroupSumVector(qualitativeStateSets.maybeStates, qualitativeStateSets.statesWithProbability1);
}
static const uint64_t NOT_IN_EC = std::numeric_limits<uint64_t>::max();
template<typename ValueType>
struct SparseMdpEndComponentInformation {
SparseMdpEndComponentInformation(storm::storage::MaximalEndComponentDecomposition<ValueType> const& endComponentDecomposition, storm::storage::BitVector const& maybeStates) : eliminatedEndComponents(false), numberOfMaybeStatesInEc(0), numberOfMaybeStatesNotInEc(0), numberOfEc(endComponentDecomposition.size()) {
// (1) Compute how many maybe states there are before each other maybe state.
maybeStatesBefore = maybeStates.getNumberOfSetBitsBeforeIndices();
// (2) Create mapping from maybe states to their MEC. If they are not contained in an MEC, their value
// is set to a special constant.
maybeStateToEc.resize(maybeStates.getNumberOfSetBits(), NOT_IN_EC);
uint64_t mecIndex = 0;
for (auto const& mec : endComponentDecomposition) {
for (auto const& stateActions : mec) {
maybeStateToEc[maybeStatesBefore[stateActions.first]] = mecIndex;
++numberOfMaybeStatesInEc;
}
++mecIndex;
}
// (3) Compute number of states not in MECs.
numberOfMaybeStatesNotInEc = maybeStateToEc.size() - numberOfMaybeStatesInEc;
}
bool isMaybeStateInEc(uint64_t maybeState) const {
return maybeStateToEc[maybeState] != NOT_IN_EC;
}
bool isStateInEc(uint64_t state) const {
return maybeStateToEc[maybeStatesBefore[state]] != NOT_IN_EC;
}
std::vector<uint64_t> getNumberOfMaybeStatesNotInEcBeforeIndices() const {
std::vector<uint64_t> result(maybeStateToEc.size());
uint64_t count = 0;
auto resultIt = result.begin();
for (auto const& e : maybeStateToEc) {
*resultIt = count;
if (e != NOT_IN_EC) {
++count;
}
++resultIt;
}
return result;
}
uint64_t getEc(uint64_t state) const {
return maybeStateToEc[maybeStatesBefore[state]];
}
uint64_t getSubmatrixRowGroupOfStateInEc(uint64_t state) const {
return numberOfMaybeStatesNotInEc + getEc(state);
}
bool eliminatedEndComponents;
std::vector<uint64_t> maybeStatesBefore;
uint64_t numberOfMaybeStatesInEc;
uint64_t numberOfMaybeStatesNotInEc;
uint64_t numberOfEc;
std::vector<uint64_t> maybeStateToEc;
};
template<typename ValueType>
SparseMdpEndComponentInformation<ValueType> eliminateEndComponents(storm::storage::MaximalEndComponentDecomposition<ValueType> const& endComponentDecomposition, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& maybeStates, storm::storage::BitVector const* sumColumns, storm::storage::BitVector const* selectedChoices, std::vector<ValueType> const* summand, storm::storage::SparseMatrix<ValueType>& submatrix, std::vector<ValueType>& b) {
SparseMdpEndComponentInformation<ValueType> result(endComponentDecomposition, maybeStates);
// (1) Compute the number of maybe states not in ECs before any other maybe state.
std::vector<uint64_t> maybeStatesNotInEcBefore = result.getNumberOfMaybeStatesNotInEcBeforeIndices();
// Create temporary vector storing possible transitions to ECs.
std::vector<std::pair<uint64_t, ValueType>> ecValuePairs;
// (2) Create the parts of the submatrix and vector b that belong to states not contained in ECs.
uint64_t numberOfStates = result.numberOfMaybeStatesNotInEc + result.numberOfEc;
STORM_LOG_TRACE("Found " << numberOfStates << " states, " << result.numberOfMaybeStatesNotInEc << " not in ECs, " << result.numberOfMaybeStatesInEc << " in ECs and " << result.numberOfEc << " ECs.");
storm::storage::SparseMatrixBuilder<ValueType> builder(0, numberOfStates, 0, true, true, numberOfStates);
b.resize(numberOfStates);
uint64_t currentRow = 0;
for (auto state : maybeStates) {
if (!result.isStateInEc(state)) {
builder.newRowGroup(currentRow);
for (uint64_t row = transitionMatrix.getRowGroupIndices()[state], endRow = transitionMatrix.getRowGroupIndices()[state + 1]; row < endRow; ++row) {
// If the choices is not in the selected ones, drop it.
if (selectedChoices && !selectedChoices->get(row)) {
continue;
}
ecValuePairs.clear();
if (summand) {
b[currentRow] += (*summand)[row];
}
for (auto const& e : transitionMatrix.getRow(row)) {
if (sumColumns && sumColumns->get(e.getColumn())) {
b[currentRow] += e.getValue();
} else if (maybeStates.get(e.getColumn())) {
// If the target state of the transition is not contained in an EC, we can just add the entry.
if (result.isStateInEc(e.getColumn())) {
builder.addNextValue(currentRow, maybeStatesNotInEcBefore[result.maybeStatesBefore[e.getColumn()]], e.getValue());
} else {
// Otherwise, we store the information that the state can go to a certain EC.
ecValuePairs.emplace_back(result.getEc(e.getColumn()), e.getValue());
}
}
}
if (!ecValuePairs.empty()) {
std::sort(ecValuePairs.begin(), ecValuePairs.end());
for (auto const& e : ecValuePairs) {
builder.addNextValue(currentRow, result.numberOfMaybeStatesNotInEc + e.first, e.second);
}
}
++currentRow;
}
}
}
// (3) Create the parts of the submatrix and vector b that belong to states contained in ECs.
for (auto const& mec : endComponentDecomposition) {
builder.newRowGroup(currentRow);
for (auto const& stateActions : mec) {
uint64_t const& state = stateActions.first;
for (uint64_t row = transitionMatrix.getRowGroupIndices()[state], endRow = transitionMatrix.getRowGroupIndices()[state + 1]; row < endRow; ++row) {
// If the choice is contained in the MEC, drop it.
if (stateActions.second.find(row) != stateActions.second.end()) {
continue;
}
// If the choices is not in the selected ones, drop it.
if (selectedChoices && !selectedChoices->get(row)) {
continue;
}
ecValuePairs.clear();
if (summand) {
b[currentRow] += (*summand)[row];
}
for (auto const& e : transitionMatrix.getRow(row)) {
if (sumColumns && sumColumns->get(e.getColumn())) {
b[currentRow] += e.getValue();
} else if (maybeStates.get(e.getColumn())) {
// If the target state of the transition is not contained in an EC, we can just add the entry.
if (result.isStateInEc(e.getColumn())) {
builder.addNextValue(currentRow, maybeStatesNotInEcBefore[result.maybeStatesBefore[e.getColumn()]], e.getValue());
} else {
// Otherwise, we store the information that the state can go to a certain EC.
ecValuePairs.emplace_back(result.getEc(e.getColumn()), e.getValue());
}
}
}
if (!ecValuePairs.empty()) {
std::sort(ecValuePairs.begin(), ecValuePairs.end());
for (auto const& e : ecValuePairs) {
builder.addNextValue(currentRow, result.numberOfMaybeStatesNotInEc + e.first, e.second);
}
}
++currentRow;
}
}
}
submatrix = builder.build();
return result;
}
template<typename ValueType>
boost::optional<SparseMdpEndComponentInformation<ValueType>> computeFixedPointSystemUntilProbabilitiesEliminateEndComponents(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, QualitativeStateSetsUntilProbabilities const& qualitativeStateSets, storm::storage::SparseMatrix<ValueType>& submatrix, std::vector<ValueType>& b) {
// Start by computing the states that are in MECs.
storm::storage::MaximalEndComponentDecomposition<ValueType> endComponentDecomposition(transitionMatrix, backwardTransitions, qualitativeStateSets.maybeStates);
// Only do more work if there are actually end-components.
if (!endComponentDecomposition.empty()) {
STORM_LOG_DEBUG("Eliminating " << endComponentDecomposition.size() << " ECs.");
return eliminateEndComponents<ValueType>(endComponentDecomposition, transitionMatrix, qualitativeStateSets.maybeStates, &qualitativeStateSets.statesWithProbability1, nullptr, nullptr, submatrix, b);
} else {
STORM_LOG_DEBUG("Not eliminating ECs as there are none.");
computeFixedPointSystemUntilProbabilities(transitionMatrix, qualitativeStateSets, submatrix, b);
return boost::none;
}
}
template<typename ValueType>
void setResultValuesWrtEndComponents(SparseMdpEndComponentInformation<ValueType> const& ecInformation, std::vector<ValueType>& result, storm::storage::BitVector const& maybeStates, std::vector<ValueType> const& fromResult) {
auto notInEcResultIt = result.begin();
for (auto state : maybeStates) {
if (ecInformation.isStateInEc(state)) {
result[state] = result[ecInformation.getSubmatrixRowGroupOfStateInEc(state)];
} else {
result[state] = *notInEcResultIt;
++notInEcResultIt;
}
}
STORM_LOG_ASSERT(notInEcResultIt == result.begin() + ecInformation.numberOfMaybeStatesNotInEc, "Mismatching iterators.");
}
template<typename ValueType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMdpPrctlHelper<ValueType>::computeUntilProbabilities(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
STORM_LOG_THROW(!qualitative || !produceScheduler, storm::exceptions::InvalidSettingsException, "Cannot produce scheduler when performing qualitative model checking only.");
// Prepare resulting vector.
std::vector<ValueType> result(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
// We need to identify the maybe states (states which have a probability for satisfying the until formula
// that is strictly between 0 and 1) and the states that satisfy the formula with probablity 1 and 0, respectively.
QualitativeStateSetsUntilProbabilities qualitativeStateSets = getQualitativeStateSetsUntilProbabilities(goal, transitionMatrix, backwardTransitions, phiStates, psiStates, hint);
// Set values of resulting vector that are known exactly.
storm::utility::vector::setVectorValues<ValueType>(result, qualitativeStateSets.statesWithProbability1, storm::utility::one<ValueType>());
// If requested, we will produce a scheduler.
std::unique_ptr<storm::storage::Scheduler<ValueType>> scheduler;
if (produceScheduler) {
scheduler = std::make_unique<storm::storage::Scheduler<ValueType>>(transitionMatrix.getRowGroupCount());
}
// Check whether we need to compute exact probabilities for some states.
if (qualitative) {
// Set the values for all maybe-states to 0.5 to indicate that their probability values are neither 0 nor 1.
storm::utility::vector::setVectorValues<ValueType>(result, qualitativeStateSets.maybeStates, storm::utility::convertNumber<ValueType>(0.5));
} else {
if (!qualitativeStateSets.maybeStates.empty()) {
// In this case we have have to compute the remaining probabilities.
// Obtain proper hint information either from the provided hint or from requirements of the solver.
SparseMdpHintType<ValueType> hintInformation = computeHints(storm::solver::EquationSystemType::UntilProbabilities, hint, goal.direction(), transitionMatrix, backwardTransitions, qualitativeStateSets.maybeStates, phiStates, qualitativeStateSets.statesWithProbability1, minMaxLinearEquationSolverFactory);
// Declare the components of the equation system we will solve.
storm::storage::SparseMatrix<ValueType> submatrix;
std::vector<ValueType> b;
// If the hint information tells us that we have to eliminate MECs, we do so now.
boost::optional<SparseMdpEndComponentInformation<ValueType>> ecInformation;
if (hintInformation.getEliminateEndComponents()) {
ecInformation = computeFixedPointSystemUntilProbabilitiesEliminateEndComponents(transitionMatrix, backwardTransitions, qualitativeStateSets, submatrix, b);
// Make sure we are not supposed to produce a scheduler if we actually eliminate end components.
STORM_LOG_THROW(!ecInformation || !ecInformation.get().eliminatedEndComponents || !produceScheduler, storm::exceptions::NotSupportedException, "Producing schedulers is not supported if end-components need to be eliminated for the solver.");
} else {
// Otherwise, we compute the standard equations.
computeFixedPointSystemUntilProbabilities(transitionMatrix, qualitativeStateSets, submatrix, b);
}
// Now compute the results for the maybe states.
MaybeStateResult<ValueType> resultForMaybeStates = computeValuesForMaybeStates(goal, submatrix, b, produceScheduler, minMaxLinearEquationSolverFactory, hintInformation);
// If we eliminated end components, we need to extract the result differently.
if (ecInformation && ecInformation.get().eliminatedEndComponents) {
setResultValuesWrtEndComponents(ecInformation.get(), result, qualitativeStateSets.maybeStates, resultForMaybeStates.getValues());
} else {
// Set values of resulting vector according to result.
storm::utility::vector::setVectorValues<ValueType>(result, qualitativeStateSets.maybeStates, resultForMaybeStates.getValues());
}
if (produceScheduler) {
extractSchedulerChoices(*scheduler, resultForMaybeStates.getScheduler(), qualitativeStateSets.maybeStates);
}
}
}
// Extend scheduler with choices for the states in the qualitative state sets.
if (produceScheduler) {
extendScheduler(*scheduler, goal, qualitativeStateSets, transitionMatrix, backwardTransitions, phiStates, psiStates);
}
// Sanity check for created scheduler.
STORM_LOG_ASSERT((!produceScheduler && !scheduler) || (!scheduler->isPartialScheduler() && scheduler->isDeterministicScheduler() && scheduler->isMemorylessScheduler()), "Unexpected format of obtained scheduler.");
// Return result.
return MDPSparseModelCheckingHelperReturnType<ValueType>(std::move(result), std::move(scheduler));
}
template<typename ValueType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMdpPrctlHelper<ValueType>::computeUntilProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
storm::solver::SolveGoal goal(dir);
return std::move(computeUntilProbabilities(goal, transitionMatrix, backwardTransitions, phiStates, psiStates, qualitative, produceScheduler, minMaxLinearEquationSolverFactory, hint));
}
template<typename ValueType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeGloballyProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& psiStates, bool qualitative, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, bool useMecBasedTechnique) {
if (useMecBasedTechnique) {
storm::storage::MaximalEndComponentDecomposition<ValueType> mecDecomposition(transitionMatrix, backwardTransitions, psiStates);
storm::storage::BitVector statesInPsiMecs(transitionMatrix.getRowGroupCount());
for (auto const& mec : mecDecomposition) {
for (auto const& stateActionsPair : mec) {
statesInPsiMecs.set(stateActionsPair.first, true);
}
}
return std::move(computeUntilProbabilities(dir, transitionMatrix, backwardTransitions, psiStates, statesInPsiMecs, qualitative, false, minMaxLinearEquationSolverFactory).values);
} else {
std::vector<ValueType> result = computeUntilProbabilities(dir == OptimizationDirection::Minimize ? OptimizationDirection::Maximize : OptimizationDirection::Minimize, transitionMatrix, backwardTransitions, storm::storage::BitVector(transitionMatrix.getRowGroupCount(), true), ~psiStates, qualitative, false, minMaxLinearEquationSolverFactory).values;
for (auto& element : result) {
element = storm::utility::one<ValueType>() - element;
}
return std::move(result);
}
}
template<typename ValueType>
template<typename RewardModelType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeInstantaneousRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, RewardModelType const& rewardModel, uint_fast64_t stepCount, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Only compute the result if the model has a state-based reward this->getModel().
STORM_LOG_THROW(rewardModel.hasStateRewards(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
// Initialize result to state rewards of the this->getModel().
std::vector<ValueType> result(rewardModel.getStateRewardVector());
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(transitionMatrix);
solver->repeatedMultiply(dir, result, nullptr, stepCount);
return result;
}
template<typename ValueType>
template<typename RewardModelType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeCumulativeRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, RewardModelType const& rewardModel, uint_fast64_t stepBound, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Only compute the result if the model has at least one reward this->getModel().
STORM_LOG_THROW(!rewardModel.empty(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
// Compute the reward vector to add in each step based on the available reward models.
std::vector<ValueType> totalRewardVector = rewardModel.getTotalRewardVector(transitionMatrix);
// Initialize result to the zero vector.
std::vector<ValueType> result(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(transitionMatrix);
solver->repeatedMultiply(dir, result, &totalRewardVector, stepBound);
return result;
}
template<typename ValueType>
template<typename RewardModelType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, RewardModelType const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
// Only compute the result if the model has at least one reward this->getModel().
STORM_LOG_THROW(!rewardModel.empty(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
return computeReachabilityRewardsHelper(storm::solver::SolveGoal(dir), transitionMatrix, backwardTransitions,
[&rewardModel] (uint_fast64_t rowCount, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& maybeStates) {
return rewardModel.getTotalRewardVector(rowCount, transitionMatrix, maybeStates);
},
targetStates, qualitative, produceScheduler, minMaxLinearEquationSolverFactory, hint);
}
template<typename ValueType>
template<typename RewardModelType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, RewardModelType const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
// Only compute the result if the model has at least one reward this->getModel().
STORM_LOG_THROW(!rewardModel.empty(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
return computeReachabilityRewardsHelper(goal, transitionMatrix, backwardTransitions,
[&rewardModel] (uint_fast64_t rowCount, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& maybeStates) {
return rewardModel.getTotalRewardVector(rowCount, transitionMatrix, maybeStates);
},
targetStates, qualitative, produceScheduler, minMaxLinearEquationSolverFactory, hint);
}
#ifdef STORM_HAVE_CARL
template<typename ValueType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::models::sparse::StandardRewardModel<storm::Interval> const& intervalRewardModel, bool lowerBoundOfIntervals, storm::storage::BitVector const& targetStates, bool qualitative, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Only compute the result if the reward model is not empty.
STORM_LOG_THROW(!intervalRewardModel.empty(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
return computeReachabilityRewardsHelper(storm::solver::SolveGoal(dir), transitionMatrix, backwardTransitions, \
[&] (uint_fast64_t rowCount, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& maybeStates) {
std::vector<ValueType> result;
result.reserve(rowCount);
std::vector<storm::Interval> subIntervalVector = intervalRewardModel.getTotalRewardVector(rowCount, transitionMatrix, maybeStates);
for (auto const& interval : subIntervalVector) {
result.push_back(lowerBoundOfIntervals ? interval.lower() : interval.upper());
}
return result;
}, \
targetStates, qualitative, false, minMaxLinearEquationSolverFactory).values;
}
template<>
std::vector<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeReachabilityRewards(OptimizationDirection, storm::storage::SparseMatrix<storm::RationalNumber> const&, storm::storage::SparseMatrix<storm::RationalNumber> const&, storm::models::sparse::StandardRewardModel<storm::Interval> const&, bool, storm::storage::BitVector const&, bool, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const&) {
STORM_LOG_THROW(false, storm::exceptions::IllegalFunctionCallException, "Computing reachability rewards is unsupported for this data type.");
}
#endif
struct QualitativeStateSetsReachabilityRewards {
storm::storage::BitVector maybeStates;
storm::storage::BitVector infinityStates;
};
template<typename ValueType>
QualitativeStateSetsReachabilityRewards getQualitativeStateSetsReachabilityRewardsFromHint(ModelCheckerHint const& hint, storm::storage::BitVector const& targetStates) {
QualitativeStateSetsReachabilityRewards result;
result.maybeStates = hint.template asExplicitModelCheckerHint<ValueType>().getMaybeStates();
result.infinityStates = ~(result.maybeStates | targetStates);
return result;
}
template<typename ValueType>
QualitativeStateSetsReachabilityRewards computeQualitativeStateSetsReachabilityRewards(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& targetStates) {
QualitativeStateSetsReachabilityRewards result;
storm::storage::BitVector trueStates(transitionMatrix.getRowGroupCount(), true);
if (goal.minimize()) {
result.infinityStates = storm::utility::graph::performProb1E(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, trueStates, targetStates);
} else {
result.infinityStates = storm::utility::graph::performProb1A(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, trueStates, targetStates);
}
result.infinityStates.complement();
result.maybeStates = ~(targetStates | result.infinityStates);
STORM_LOG_INFO("Found " << result.infinityStates.getNumberOfSetBits() << " 'infinity' states.");
STORM_LOG_INFO("Found " << targetStates.getNumberOfSetBits() << " 'target' states.");
STORM_LOG_INFO("Found " << result.maybeStates.getNumberOfSetBits() << " 'maybe' states.");
return result;
}
template<typename ValueType>
QualitativeStateSetsReachabilityRewards getQualitativeStateSetsReachabilityRewards(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& targetStates, ModelCheckerHint const& hint) {
if (hint.isExplicitModelCheckerHint() && hint.template asExplicitModelCheckerHint<ValueType>().getComputeOnlyMaybeStates()) {
return getQualitativeStateSetsReachabilityRewardsFromHint<ValueType>(hint, targetStates);
} else {
return computeQualitativeStateSetsReachabilityRewards(goal, transitionMatrix, backwardTransitions, targetStates);
}
}
template<typename ValueType>
void extendScheduler(storm::storage::Scheduler<ValueType>& scheduler, storm::solver::SolveGoal const& goal, QualitativeStateSetsReachabilityRewards const& qualitativeStateSets, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& targetStates) {
// Finally, if we need to produce a scheduler, we also need to figure out the parts of the scheduler for
// the states with reward infinity. Moreover, we have to set some arbitrary choice for the remaining states
// to obtain a fully defined scheduler.
if (!goal.minimize()) {
storm::utility::graph::computeSchedulerProb0E(qualitativeStateSets.infinityStates, transitionMatrix, scheduler);
} else {
for (auto const& state : qualitativeStateSets.infinityStates) {
scheduler.setChoice(0, state);
}
}
for (auto const& state : targetStates) {
scheduler.setChoice(0, state);
}
}
template<typename ValueType>
void extractSchedulerChoices(storm::storage::Scheduler<ValueType>& scheduler, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<uint_fast64_t> const& subChoices, storm::storage::BitVector const& maybeStates, boost::optional<storm::storage::BitVector> const& selectedChoices) {
auto subChoiceIt = subChoices.begin();
if (selectedChoices) {
for (auto maybeState : maybeStates) {
// find the rowindex that corresponds to the selected row of the submodel
uint_fast64_t firstRowIndex = transitionMatrix.getRowGroupIndices()[maybeState];
uint_fast64_t selectedRowIndex = selectedChoices->getNextSetIndex(firstRowIndex);
for (uint_fast64_t choice = 0; choice < *subChoiceIt; ++choice) {
selectedRowIndex = selectedChoices->getNextSetIndex(selectedRowIndex + 1);
}
scheduler.setChoice(selectedRowIndex - firstRowIndex, maybeState);
++subChoiceIt;
}
} else {
for (auto maybeState : maybeStates) {
scheduler.setChoice(*subChoiceIt, maybeState);
++subChoiceIt;
}
}
assert(subChoiceIt == subChoices.end());
}
template<typename ValueType>
void computeFixedPointSystemReachabilityRewards(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, QualitativeStateSetsReachabilityRewards const& qualitativeStateSets, boost::optional<storm::storage::BitVector> const& selectedChoices, std::function<std::vector<ValueType>(uint_fast64_t, storm::storage::SparseMatrix<ValueType> const&, storm::storage::BitVector const&)> const& totalStateRewardVectorGetter, storm::storage::SparseMatrix<ValueType>& submatrix, std::vector<ValueType>& b) {
// Remove rows and columns from the original transition probability matrix for states whose reward values are already known.
// If there are infinity states, we additionally have to remove choices of maybeState that lead to infinity.
if (qualitativeStateSets.infinityStates.empty()) {
submatrix = transitionMatrix.getSubmatrix(true, qualitativeStateSets.maybeStates, qualitativeStateSets.maybeStates, false);
b = totalStateRewardVectorGetter(submatrix.getRowCount(), transitionMatrix, qualitativeStateSets.maybeStates);
} else {
submatrix = transitionMatrix.getSubmatrix(false, *selectedChoices, qualitativeStateSets.maybeStates, false);
b = totalStateRewardVectorGetter(transitionMatrix.getRowCount(), transitionMatrix, storm::storage::BitVector(transitionMatrix.getRowGroupCount(), true));
storm::utility::vector::filterVectorInPlace(b, *selectedChoices);
}
}
template<typename ValueType>
boost::optional<SparseMdpEndComponentInformation<ValueType>> computeFixedPointSystemReachabilityRewardsEliminateEndComponents(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, QualitativeStateSetsReachabilityRewards const& qualitativeStateSets, boost::optional<storm::storage::BitVector> const& selectedChoices, std::function<std::vector<ValueType>(uint_fast64_t, storm::storage::SparseMatrix<ValueType> const&, storm::storage::BitVector const&)> const& totalStateRewardVectorGetter, storm::storage::SparseMatrix<ValueType>& submatrix, std::vector<ValueType>& b) {
// Start by computing the choices with reward 0, as we only want ECs within this fragment.
storm::storage::BitVector zeroRewardChoices(transitionMatrix.getRowCount());
// Get the rewards of all choices.
std::vector<ValueType> rewardVector = totalStateRewardVectorGetter(transitionMatrix.getRowCount(), transitionMatrix, storm::storage::BitVector(transitionMatrix.getRowGroupCount(), true));
uint64_t index = 0;
for (auto const& e : rewardVector) {
if (storm::utility::isZero(e)) {
zeroRewardChoices.set(index);
}
++index;
}
// Compute the states that have some zero reward choice.
storm::storage::BitVector candidateStates(qualitativeStateSets.maybeStates);
for (auto state : qualitativeStateSets.maybeStates) {
bool keepState = false;
for (auto row = transitionMatrix.getRowGroupIndices()[state], rowEnd = transitionMatrix.getRowGroupIndices()[state + 1]; row < rowEnd; ++row) {
if (zeroRewardChoices.get(row)) {
keepState = true;
break;
}
}
if (!keepState) {
candidateStates.set(state, false);
}
}
bool doDecomposition = !candidateStates.empty();
storm::storage::MaximalEndComponentDecomposition<ValueType> endComponentDecomposition;
if (doDecomposition) {
// Then compute the states that are in MECs with zero reward.
endComponentDecomposition = storm::storage::MaximalEndComponentDecomposition<ValueType>(transitionMatrix, backwardTransitions, candidateStates, zeroRewardChoices);
}
// Only do more work if there are actually end-components.
if (doDecomposition && !endComponentDecomposition.empty()) {
STORM_LOG_DEBUG("Eliminating " << endComponentDecomposition.size() << " ECs.");
return eliminateEndComponents<ValueType>(endComponentDecomposition, transitionMatrix, qualitativeStateSets.maybeStates, nullptr, selectedChoices ? &selectedChoices.get() : nullptr, &rewardVector, submatrix, b);
} else {
STORM_LOG_DEBUG("Not eliminating ECs as there are none.");
computeFixedPointSystemReachabilityRewards(transitionMatrix, qualitativeStateSets, selectedChoices, totalStateRewardVectorGetter, submatrix, b);
return boost::none;
}
}
template<typename ValueType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMdpPrctlHelper<ValueType>::computeReachabilityRewardsHelper(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::function<std::vector<ValueType>(uint_fast64_t, storm::storage::SparseMatrix<ValueType> const&, storm::storage::BitVector const&)> const& totalStateRewardVectorGetter, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint) {
// Prepare resulting vector.
std::vector<ValueType> result(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
// Determine which states have a reward that is infinity or less than infinity.
QualitativeStateSetsReachabilityRewards qualitativeStateSets = getQualitativeStateSetsReachabilityRewards(goal, transitionMatrix, backwardTransitions, targetStates, hint);
storm::utility::vector::setVectorValues(result, qualitativeStateSets.infinityStates, storm::utility::infinity<ValueType>());
// If requested, we will produce a scheduler.
std::unique_ptr<storm::storage::Scheduler<ValueType>> scheduler;
if (produceScheduler) {
scheduler = std::make_unique<storm::storage::Scheduler<ValueType>>(transitionMatrix.getRowGroupCount());
}
// Check whether we need to compute exact rewards for some states.
if (qualitative) {
STORM_LOG_INFO("The rewards for the initial states were determined in a preprocessing step. No exact rewards were computed.");
// Set the values for all maybe-states to 1 to indicate that their reward values
// are neither 0 nor infinity.
storm::utility::vector::setVectorValues<ValueType>(result, qualitativeStateSets.maybeStates, storm::utility::one<ValueType>());
} else {
if (!qualitativeStateSets.maybeStates.empty()) {
// In this case we have to compute the reward values for the remaining states.
// Store the choices that lead to non-infinity values. If none, all choices im maybe states can be selected.
boost::optional<storm::storage::BitVector> selectedChoices;
if (!qualitativeStateSets.infinityStates.empty()) {
selectedChoices = transitionMatrix.getRowFilter(qualitativeStateSets.maybeStates, ~qualitativeStateSets.infinityStates);
}
// Obtain proper hint information either from the provided hint or from requirements of the solver.
SparseMdpHintType<ValueType> hintInformation = computeHints(storm::solver::EquationSystemType::ReachabilityRewards, hint, goal.direction(), transitionMatrix, backwardTransitions, qualitativeStateSets.maybeStates, ~targetStates, targetStates, minMaxLinearEquationSolverFactory, selectedChoices);
// Declare the components of the equation system we will solve.
storm::storage::SparseMatrix<ValueType> submatrix;
std::vector<ValueType> b;
// If the hint information tells us that we have to eliminate MECs, we do so now.
boost::optional<SparseMdpEndComponentInformation<ValueType>> ecInformation;
if (hintInformation.getEliminateEndComponents()) {
ecInformation = computeFixedPointSystemReachabilityRewardsEliminateEndComponents(transitionMatrix, backwardTransitions, qualitativeStateSets, selectedChoices, totalStateRewardVectorGetter, submatrix, b);
// Make sure we are not supposed to produce a scheduler if we actually eliminate end components.
STORM_LOG_THROW(!ecInformation || !ecInformation.get().eliminatedEndComponents || !produceScheduler, storm::exceptions::NotSupportedException, "Producing schedulers is not supported if end-components need to be eliminated for the solver.");
} else {
// Otherwise, we compute the standard equations.
computeFixedPointSystemReachabilityRewards(transitionMatrix, qualitativeStateSets, selectedChoices, totalStateRewardVectorGetter, submatrix, b);
}
// Now compute the results for the maybe states.
MaybeStateResult<ValueType> resultForMaybeStates = computeValuesForMaybeStates(goal, submatrix, b, produceScheduler, minMaxLinearEquationSolverFactory, hintInformation);
// If we eliminated end components, we need to extract the result differently.
if (ecInformation && ecInformation.get().eliminatedEndComponents) {
setResultValuesWrtEndComponents(ecInformation.get(), result, qualitativeStateSets.maybeStates, resultForMaybeStates.getValues());
} else {
// Set values of resulting vector according to result.
storm::utility::vector::setVectorValues<ValueType>(result, qualitativeStateSets.maybeStates, resultForMaybeStates.getValues());
}
if (produceScheduler) {
extractSchedulerChoices(*scheduler, transitionMatrix, resultForMaybeStates.getScheduler(), qualitativeStateSets.maybeStates, selectedChoices);
}
}
}
// Extend scheduler with choices for the states in the qualitative state sets.
if (produceScheduler) {
extendScheduler(*scheduler, goal, qualitativeStateSets, transitionMatrix, targetStates);
}
// Sanity check for created scheduler.
STORM_LOG_ASSERT((!produceScheduler && !scheduler) || (!scheduler->isPartialScheduler() && scheduler->isDeterministicScheduler() && scheduler->isMemorylessScheduler()), "Unexpected format of obtained scheduler.");
return MDPSparseModelCheckingHelperReturnType<ValueType>(std::move(result), std::move(scheduler));
}
template<typename ValueType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeLongRunAverageProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& psiStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// If there are no goal states, we avoid the computation and directly return zero.
if (psiStates.empty()) {
return std::vector<ValueType>(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
}
// Likewise, if all bits are set, we can avoid the computation and set.
if (psiStates.full()) {
return std::vector<ValueType>(transitionMatrix.getRowGroupCount(), storm::utility::one<ValueType>());
}
// Reduce long run average probabilities to long run average rewards by
// building a reward model assigning one reward to every psi state
std::vector<ValueType> stateRewards(psiStates.size(), storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(stateRewards, psiStates, storm::utility::one<ValueType>());
storm::models::sparse::StandardRewardModel<ValueType> rewardModel(std::move(stateRewards));
return computeLongRunAverageRewards(dir, transitionMatrix, backwardTransitions, rewardModel, minMaxLinearEquationSolverFactory);
}
template<typename ValueType>
template<typename RewardModelType>
std::vector<ValueType> SparseMdpPrctlHelper<ValueType>::computeLongRunAverageRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, RewardModelType const& rewardModel, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
uint64_t numberOfStates = transitionMatrix.getRowGroupCount();
// Start by decomposing the MDP into its MECs.
storm::storage::MaximalEndComponentDecomposition<ValueType> mecDecomposition(transitionMatrix, backwardTransitions);
// Get some data members for convenience.
std::vector<uint_fast64_t> const& nondeterministicChoiceIndices = transitionMatrix.getRowGroupIndices();
ValueType zero = storm::utility::zero<ValueType>();
//first calculate LRA for the Maximal End Components.
storm::storage::BitVector statesInMecs(numberOfStates);
std::vector<uint_fast64_t> stateToMecIndexMap(transitionMatrix.getColumnCount());
std::vector<ValueType> lraValuesForEndComponents(mecDecomposition.size(), zero);
for (uint_fast64_t currentMecIndex = 0; currentMecIndex < mecDecomposition.size(); ++currentMecIndex) {
storm::storage::MaximalEndComponent const& mec = mecDecomposition[currentMecIndex];
lraValuesForEndComponents[currentMecIndex] = computeLraForMaximalEndComponent(dir, transitionMatrix, rewardModel, mec, minMaxLinearEquationSolverFactory);
// Gather information for later use.
for (auto const& stateChoicesPair : mec) {
statesInMecs.set(stateChoicesPair.first);
stateToMecIndexMap[stateChoicesPair.first] = currentMecIndex;
}
}
// For fast transition rewriting, we build some auxiliary data structures.
storm::storage::BitVector statesNotContainedInAnyMec = ~statesInMecs;
uint_fast64_t firstAuxiliaryStateIndex = statesNotContainedInAnyMec.getNumberOfSetBits();
uint_fast64_t lastStateNotInMecs = 0;
uint_fast64_t numberOfStatesNotInMecs = 0;
std::vector<uint_fast64_t> statesNotInMecsBeforeIndex;
statesNotInMecsBeforeIndex.reserve(numberOfStates);
for (auto state : statesNotContainedInAnyMec) {
while (lastStateNotInMecs <= state) {
statesNotInMecsBeforeIndex.push_back(numberOfStatesNotInMecs);
++lastStateNotInMecs;
}
++numberOfStatesNotInMecs;
}
// Finally, we are ready to create the SSP matrix and right-hand side of the SSP.
std::vector<ValueType> b;
uint64_t numberOfSspStates = numberOfStatesNotInMecs + mecDecomposition.size();
typename storm::storage::SparseMatrixBuilder<ValueType> sspMatrixBuilder(0, numberOfSspStates, 0, false, true, numberOfSspStates);
// If the source state is not contained in any MEC, we copy its choices (and perform the necessary modifications).
uint_fast64_t currentChoice = 0;
for (auto state : statesNotContainedInAnyMec) {
sspMatrixBuilder.newRowGroup(currentChoice);
for (uint_fast64_t choice = nondeterministicChoiceIndices[state]; choice < nondeterministicChoiceIndices[state + 1]; ++choice, ++currentChoice) {
std::vector<ValueType> auxiliaryStateToProbabilityMap(mecDecomposition.size());
b.push_back(storm::utility::zero<ValueType>());
for (auto element : transitionMatrix.getRow(choice)) {
if (statesNotContainedInAnyMec.get(element.getColumn())) {
// If the target state is not contained in an MEC, we can copy over the entry.
sspMatrixBuilder.addNextValue(currentChoice, statesNotInMecsBeforeIndex[element.getColumn()], element.getValue());
} else {
// If the target state is contained in MEC i, we need to add the probability to the corresponding field in the vector
// so that we are able to write the cumulative probability to the MEC into the matrix.
auxiliaryStateToProbabilityMap[stateToMecIndexMap[element.getColumn()]] += element.getValue();
}
}
// Now insert all (cumulative) probability values that target an MEC.
for (uint_fast64_t mecIndex = 0; mecIndex < auxiliaryStateToProbabilityMap.size(); ++mecIndex) {
if (auxiliaryStateToProbabilityMap[mecIndex] != 0) {
sspMatrixBuilder.addNextValue(currentChoice, firstAuxiliaryStateIndex + mecIndex, auxiliaryStateToProbabilityMap[mecIndex]);
}
}
}
}
// Now we are ready to construct the choices for the auxiliary states.
for (uint_fast64_t mecIndex = 0; mecIndex < mecDecomposition.size(); ++mecIndex) {
storm::storage::MaximalEndComponent const& mec = mecDecomposition[mecIndex];
sspMatrixBuilder.newRowGroup(currentChoice);
for (auto const& stateChoicesPair : mec) {
uint_fast64_t state = stateChoicesPair.first;
boost::container::flat_set<uint_fast64_t> const& choicesInMec = stateChoicesPair.second;
for (uint_fast64_t choice = nondeterministicChoiceIndices[state]; choice < nondeterministicChoiceIndices[state + 1]; ++choice) {
// If the choice is not contained in the MEC itself, we have to add a similar distribution to the auxiliary state.
if (choicesInMec.find(choice) == choicesInMec.end()) {
std::vector<ValueType> auxiliaryStateToProbabilityMap(mecDecomposition.size());
b.push_back(storm::utility::zero<ValueType>());
for (auto element : transitionMatrix.getRow(choice)) {
if (statesNotContainedInAnyMec.get(element.getColumn())) {
// If the target state is not contained in an MEC, we can copy over the entry.
sspMatrixBuilder.addNextValue(currentChoice, statesNotInMecsBeforeIndex[element.getColumn()], element.getValue());
} else {
// If the target state is contained in MEC i, we need to add the probability to the corresponding field in the vector
// so that we are able to write the cumulative probability to the MEC into the matrix.
auxiliaryStateToProbabilityMap[stateToMecIndexMap[element.getColumn()]] += element.getValue();
}
}
// Now insert all (cumulative) probability values that target an MEC.
for (uint_fast64_t targetMecIndex = 0; targetMecIndex < auxiliaryStateToProbabilityMap.size(); ++targetMecIndex) {
if (auxiliaryStateToProbabilityMap[targetMecIndex] != 0) {
sspMatrixBuilder.addNextValue(currentChoice, firstAuxiliaryStateIndex + targetMecIndex, auxiliaryStateToProbabilityMap[targetMecIndex]);
}
}
++currentChoice;
}
}
}
// For each auxiliary state, there is the option to achieve the reward value of the LRA associated with the MEC.
++currentChoice;
b.push_back(lraValuesForEndComponents[mecIndex]);
}
// Finalize the matrix and solve the corresponding system of equations.
storm::storage::SparseMatrix<ValueType> sspMatrix = sspMatrixBuilder.build(currentChoice, numberOfSspStates, numberOfSspStates);
// Check for requirements of the solver.
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(storm::solver::EquationSystemType::StochasticShortestPath);
STORM_LOG_THROW(requirements.empty(), storm::exceptions::UncheckedRequirementException, "Cannot establish requirements for solver.");
std::vector<ValueType> sspResult(numberOfSspStates);
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(std::move(sspMatrix));
solver->setRequirementsChecked();
solver->solveEquations(dir, sspResult, b);
// Prepare result vector.
std::vector<ValueType> result(numberOfStates, zero);
// Set the values for states not contained in MECs.
storm::utility::vector::setVectorValues(result, statesNotContainedInAnyMec, sspResult);
// Set the values for all states in MECs.
for (auto state : statesInMecs) {
result[state] = sspResult[firstAuxiliaryStateIndex + stateToMecIndexMap[state]];
}
return result;
}
template<typename ValueType>
template<typename RewardModelType>
ValueType SparseMdpPrctlHelper<ValueType>::computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, RewardModelType const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// If the mec only consists of a single state, we compute the LRA value directly
if (++mec.begin() == mec.end()) {
uint64_t state = mec.begin()->first;
auto choiceIt = mec.begin()->second.begin();
ValueType result = rewardModel.getTotalStateActionReward(state, *choiceIt, transitionMatrix);
for (++choiceIt; choiceIt != mec.begin()->second.end(); ++choiceIt) {
if (storm::solver::minimize(dir)) {
result = std::min(result, rewardModel.getTotalStateActionReward(state, *choiceIt, transitionMatrix));
} else {
result = std::max(result, rewardModel.getTotalStateActionReward(state, *choiceIt, transitionMatrix));
}
}
return result;
}
// Solve MEC with the method specified in the settings
storm::solver::LraMethod method = storm::settings::getModule<storm::settings::modules::MinMaxEquationSolverSettings>().getLraMethod();
if (method == storm::solver::LraMethod::LinearProgramming) {
return computeLraForMaximalEndComponentLP(dir, transitionMatrix, rewardModel, mec);
} else if (method == storm::solver::LraMethod::ValueIteration) {
return computeLraForMaximalEndComponentVI(dir, transitionMatrix, rewardModel, mec, minMaxLinearEquationSolverFactory);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidSettingsException, "Unsupported technique.");
}
}
template<typename ValueType>
template<typename RewardModelType>
ValueType SparseMdpPrctlHelper<ValueType>::computeLraForMaximalEndComponentVI(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, RewardModelType const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Initialize data about the mec
storm::storage::BitVector mecStates(transitionMatrix.getRowGroupCount(), false);
storm::storage::BitVector mecChoices(transitionMatrix.getRowCount(), false);
for (auto const& stateChoicesPair : mec) {
mecStates.set(stateChoicesPair.first);
for (auto const& choice : stateChoicesPair.second) {
mecChoices.set(choice);
}
}
boost::container::flat_map<uint64_t, uint64_t> toSubModelStateMapping;
uint64_t currState = 0;
toSubModelStateMapping.reserve(mecStates.getNumberOfSetBits());
for (auto const& mecState : mecStates) {
toSubModelStateMapping.insert(std::pair<uint64_t, uint64_t>(mecState, currState));
++currState;
}
// Get a transition matrix that only considers the states and choices within the MEC
storm::storage::SparseMatrixBuilder<ValueType> mecTransitionBuilder(mecChoices.getNumberOfSetBits(), mecStates.getNumberOfSetBits(), 0, true, true, mecStates.getNumberOfSetBits());
std::vector<ValueType> choiceRewards;
choiceRewards.reserve(mecChoices.getNumberOfSetBits());
uint64_t currRow = 0;
ValueType selfLoopProb = storm::utility::convertNumber<ValueType>(0.1); // todo try other values
ValueType scalingFactor = storm::utility::one<ValueType>() - selfLoopProb;
for (auto const& mecState : mecStates) {
mecTransitionBuilder.newRowGroup(currRow);
uint64_t groupStart = transitionMatrix.getRowGroupIndices()[mecState];
uint64_t groupEnd = transitionMatrix.getRowGroupIndices()[mecState + 1];
for (uint64_t choice = mecChoices.getNextSetIndex(groupStart); choice < groupEnd; choice = mecChoices.getNextSetIndex(choice + 1)) {
bool insertedDiagElement = false;
for (auto const& entry : transitionMatrix.getRow(choice)) {
uint64_t column = toSubModelStateMapping[entry.getColumn()];
if (!insertedDiagElement && entry.getColumn() > mecState) {
mecTransitionBuilder.addNextValue(currRow, toSubModelStateMapping[mecState], selfLoopProb);
insertedDiagElement = true;
}
if (!insertedDiagElement && entry.getColumn() == mecState) {
mecTransitionBuilder.addNextValue(currRow, column, selfLoopProb + scalingFactor * entry.getValue());
insertedDiagElement = true;
} else {
mecTransitionBuilder.addNextValue(currRow, column, scalingFactor * entry.getValue());
}
}
if (!insertedDiagElement) {
mecTransitionBuilder.addNextValue(currRow, toSubModelStateMapping[mecState], selfLoopProb);
}
// Compute the rewards obtained for this choice
choiceRewards.push_back(scalingFactor * rewardModel.getTotalStateActionReward(mecState, choice, transitionMatrix));
++currRow;
}
}
auto mecTransitions = mecTransitionBuilder.build();
STORM_LOG_ASSERT(mecTransitions.isProbabilistic(), "The MEC-Matrix is not probabilistic.");
// start the iterations
ValueType precision = storm::utility::convertNumber<ValueType>(storm::settings::getModule<storm::settings::modules::MinMaxEquationSolverSettings>().getPrecision());
std::vector<ValueType> x(mecTransitions.getRowGroupCount(), storm::utility::zero<ValueType>());
std::vector<ValueType> xPrime = x;
auto solver = minMaxLinearEquationSolverFactory.create(std::move(mecTransitions));
solver->setCachingEnabled(true);
ValueType maxDiff, minDiff;
while (true) {
// Compute the obtained rewards for the next step
solver->repeatedMultiply(dir, x, &choiceRewards, 1);
// update xPrime and check for convergence
// to avoid large (and numerically unstable) x-values, we substract a reference value.
auto xIt = x.begin();
auto xPrimeIt = xPrime.begin();
ValueType refVal = *xIt;
maxDiff = *xIt - *xPrimeIt;
minDiff = maxDiff;
*xIt -= refVal;
*xPrimeIt = *xIt;
for (++xIt, ++xPrimeIt; xIt != x.end(); ++xIt, ++xPrimeIt) {
ValueType diff = *xIt - *xPrimeIt;
maxDiff = std::max(maxDiff, diff);
minDiff = std::min(minDiff, diff);
*xIt -= refVal;
*xPrimeIt = *xIt;
}
if ((maxDiff - minDiff) < precision) {
break;
}
}
return (maxDiff + minDiff) / (storm::utility::convertNumber<ValueType>(2.0) * scalingFactor);
}
template<typename ValueType>
template<typename RewardModelType>
ValueType SparseMdpPrctlHelper<ValueType>::computeLraForMaximalEndComponentLP(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, RewardModelType const& rewardModel, storm::storage::MaximalEndComponent const& mec) {
std::shared_ptr<storm::solver::LpSolver<ValueType>> solver = storm::utility::solver::getLpSolver<ValueType>("LRA for MEC");
solver->setOptimizationDirection(invert(dir));
// First, we need to create the variables for the problem.
std::map<uint_fast64_t, storm::expressions::Variable> stateToVariableMap;
for (auto const& stateChoicesPair : mec) {
std::string variableName = "h" + std::to_string(stateChoicesPair.first);
stateToVariableMap[stateChoicesPair.first] = solver->addUnboundedContinuousVariable(variableName);
}
storm::expressions::Variable lambda = solver->addUnboundedContinuousVariable("L", 1);
solver->update();
// Now we encode the problem as constraints.
for (auto const& stateChoicesPair : mec) {
uint_fast64_t state = stateChoicesPair.first;
// Now, based on the type of the state, create a suitable constraint.
for (auto choice : stateChoicesPair.second) {
storm::expressions::Expression constraint = -lambda;
for (auto element : transitionMatrix.getRow(choice)) {
constraint = constraint + stateToVariableMap.at(element.getColumn()) * solver->getConstant(element.getValue());
}
typename RewardModelType::ValueType r = rewardModel.getTotalStateActionReward(state, choice, transitionMatrix);
constraint = solver->getConstant(r) + constraint;
if (dir == OptimizationDirection::Minimize) {
constraint = stateToVariableMap.at(state) <= constraint;
} else {
constraint = stateToVariableMap.at(state) >= constraint;
}
solver->addConstraint("state" + std::to_string(state) + "," + std::to_string(choice), constraint);
}
}
solver->optimize();
return solver->getContinuousValue(lambda);
}
template<typename ValueType>
std::unique_ptr<CheckResult> SparseMdpPrctlHelper<ValueType>::computeConditionalProbabilities(OptimizationDirection dir, storm::storage::sparse::state_type initialState, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& targetStates, storm::storage::BitVector const& conditionStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now();
// For the max-case, we can simply take the given target states. For the min-case, however, we need to
// find the MECs of non-target states and make them the new target states.
storm::storage::BitVector fixedTargetStates;
if (dir == OptimizationDirection::Maximize) {
fixedTargetStates = targetStates;
} else {
fixedTargetStates = storm::storage::BitVector(targetStates.size());
storm::storage::MaximalEndComponentDecomposition<ValueType> mecDecomposition(transitionMatrix, backwardTransitions, ~targetStates);
for (auto const& mec : mecDecomposition) {
for (auto const& stateActionsPair : mec) {
fixedTargetStates.set(stateActionsPair.first);
}
}
}
storm::storage::BitVector allStates(fixedTargetStates.size(), true);
// Extend the target states by computing all states that have probability 1 to go to a target state
// under *all* schedulers.
fixedTargetStates = storm::utility::graph::performProb1A(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, allStates, fixedTargetStates);
// We solve the max-case and later adjust the result if the optimization direction was to minimize.
storm::storage::BitVector initialStatesBitVector(transitionMatrix.getRowGroupCount());
initialStatesBitVector.set(initialState);
// Extend the condition states by computing all states that have probability 1 to go to a condition state
// under *all* schedulers.
storm::storage::BitVector extendedConditionStates = storm::utility::graph::performProb1A(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, allStates, conditionStates);
STORM_LOG_DEBUG("Computing probabilities to satisfy condition.");
std::chrono::high_resolution_clock::time_point conditionStart = std::chrono::high_resolution_clock::now();
std::vector<ValueType> conditionProbabilities = std::move(computeUntilProbabilities(OptimizationDirection::Maximize, transitionMatrix, backwardTransitions, allStates, extendedConditionStates, false, false, minMaxLinearEquationSolverFactory).values);
std::chrono::high_resolution_clock::time_point conditionEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_DEBUG("Computed probabilities to satisfy for condition in " << std::chrono::duration_cast<std::chrono::milliseconds>(conditionEnd - conditionStart).count() << "ms.");
// If the conditional probability is undefined for the initial state, we return directly.
if (storm::utility::isZero(conditionProbabilities[initialState])) {
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, storm::utility::infinity<ValueType>()));
}
STORM_LOG_DEBUG("Computing probabilities to reach target.");
std::chrono::high_resolution_clock::time_point targetStart = std::chrono::high_resolution_clock::now();
std::vector<ValueType> targetProbabilities = std::move(computeUntilProbabilities(OptimizationDirection::Maximize, transitionMatrix, backwardTransitions, allStates, fixedTargetStates, false, false, minMaxLinearEquationSolverFactory).values);
std::chrono::high_resolution_clock::time_point targetEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_DEBUG("Computed probabilities to reach target in " << std::chrono::duration_cast<std::chrono::milliseconds>(targetEnd - targetStart).count() << "ms.");
storm::storage::BitVector statesWithProbabilityGreater0E(transitionMatrix.getRowGroupCount(), true);
storm::storage::sparse::state_type state = 0;
for (auto const& element : conditionProbabilities) {
if (storm::utility::isZero(element)) {
statesWithProbabilityGreater0E.set(state, false);
}
++state;
}
// Determine those states that need to be equipped with a restart mechanism.
STORM_LOG_DEBUG("Computing problematic states.");
storm::storage::BitVector pureResetStates = storm::utility::graph::performProb0A(backwardTransitions, allStates, extendedConditionStates);
storm::storage::BitVector problematicStates = storm::utility::graph::performProb0E(transitionMatrix, transitionMatrix.getRowGroupIndices(), backwardTransitions, allStates, extendedConditionStates | fixedTargetStates);
// Otherwise, we build the transformed MDP.
storm::storage::BitVector relevantStates = storm::utility::graph::getReachableStates(transitionMatrix, initialStatesBitVector, allStates, extendedConditionStates | fixedTargetStates | pureResetStates);
STORM_LOG_TRACE("Found " << relevantStates.getNumberOfSetBits() << " relevant states for conditional probability computation.");
std::vector<uint_fast64_t> numberOfStatesBeforeRelevantStates = relevantStates.getNumberOfSetBitsBeforeIndices();
storm::storage::sparse::state_type newGoalState = relevantStates.getNumberOfSetBits();
storm::storage::sparse::state_type newStopState = newGoalState + 1;
storm::storage::sparse::state_type newFailState = newStopState + 1;
// Build the transitions of the (relevant) states of the original model.
storm::storage::SparseMatrixBuilder<ValueType> builder(0, newFailState + 1, 0, true, true);
uint_fast64_t currentRow = 0;
for (auto state : relevantStates) {
builder.newRowGroup(currentRow);
if (fixedTargetStates.get(state)) {
if (!storm::utility::isZero(conditionProbabilities[state])) {
builder.addNextValue(currentRow, newGoalState, conditionProbabilities[state]);
}
if (!storm::utility::isOne(conditionProbabilities[state])) {
builder.addNextValue(currentRow, newFailState, storm::utility::one<ValueType>() - conditionProbabilities[state]);
}
++currentRow;
} else if (extendedConditionStates.get(state)) {
if (!storm::utility::isZero(targetProbabilities[state])) {
builder.addNextValue(currentRow, newGoalState, targetProbabilities[state]);
}
if (!storm::utility::isOne(targetProbabilities[state])) {
builder.addNextValue(currentRow, newStopState, storm::utility::one<ValueType>() - targetProbabilities[state]);
}
++currentRow;
} else if (pureResetStates.get(state)) {
builder.addNextValue(currentRow, numberOfStatesBeforeRelevantStates[initialState], storm::utility::one<ValueType>());
++currentRow;
} else {
for (uint_fast64_t row = transitionMatrix.getRowGroupIndices()[state]; row < transitionMatrix.getRowGroupIndices()[state + 1]; ++row) {
for (auto const& successorEntry : transitionMatrix.getRow(row)) {
builder.addNextValue(currentRow, numberOfStatesBeforeRelevantStates[successorEntry.getColumn()], successorEntry.getValue());
}
++currentRow;
}
if (problematicStates.get(state)) {
builder.addNextValue(currentRow, numberOfStatesBeforeRelevantStates[initialState], storm::utility::one<ValueType>());
++currentRow;
}
}
}
// Now build the transitions of the newly introduced states.
builder.newRowGroup(currentRow);
builder.addNextValue(currentRow, newGoalState, storm::utility::one<ValueType>());
++currentRow;
builder.newRowGroup(currentRow);
builder.addNextValue(currentRow, newStopState, storm::utility::one<ValueType>());
++currentRow;
builder.newRowGroup(currentRow);
builder.addNextValue(currentRow, numberOfStatesBeforeRelevantStates[initialState], storm::utility::one<ValueType>());
++currentRow;
std::chrono::high_resolution_clock::time_point end = std::chrono::high_resolution_clock::now();
STORM_LOG_DEBUG("Computed transformed model in " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms.");
// Finally, build the matrix and dispatch the query as a reachability query.
STORM_LOG_DEBUG("Computing conditional probabilties.");
storm::storage::BitVector newGoalStates(newFailState + 1);
newGoalStates.set(newGoalState);
storm::storage::SparseMatrix<ValueType> newTransitionMatrix = builder.build();
STORM_LOG_DEBUG("Transformed model has " << newTransitionMatrix.getRowGroupCount() << " states and " << newTransitionMatrix.getNonzeroEntryCount() << " transitions.");
storm::storage::SparseMatrix<ValueType> newBackwardTransitions = newTransitionMatrix.transpose(true);
std::chrono::high_resolution_clock::time_point conditionalStart = std::chrono::high_resolution_clock::now();
std::vector<ValueType> goalProbabilities = std::move(computeUntilProbabilities(OptimizationDirection::Maximize, newTransitionMatrix, newBackwardTransitions, storm::storage::BitVector(newFailState + 1, true), newGoalStates, false, false, minMaxLinearEquationSolverFactory).values);
std::chrono::high_resolution_clock::time_point conditionalEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_DEBUG("Computed conditional probabilities in transformed model in " << std::chrono::duration_cast<std::chrono::milliseconds>(conditionalEnd - conditionalStart).count() << "ms.");
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(initialState, dir == OptimizationDirection::Maximize ? goalProbabilities[numberOfStatesBeforeRelevantStates[initialState]] : storm::utility::one<ValueType>() - goalProbabilities[numberOfStatesBeforeRelevantStates[initialState]]));
}
template class SparseMdpPrctlHelper<double>;
template std::vector<double> SparseMdpPrctlHelper<double>::computeInstantaneousRewards(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::models::sparse::StandardRewardModel<double> const& rewardModel, uint_fast64_t stepCount, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template std::vector<double> SparseMdpPrctlHelper<double>::computeCumulativeRewards(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::models::sparse::StandardRewardModel<double> const& rewardModel, uint_fast64_t stepBound, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template MDPSparseModelCheckingHelperReturnType<double> SparseMdpPrctlHelper<double>::computeReachabilityRewards(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::SparseMatrix<double> const& backwardTransitions, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint);
template MDPSparseModelCheckingHelperReturnType<double> SparseMdpPrctlHelper<double>::computeReachabilityRewards(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::SparseMatrix<double> const& backwardTransitions, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint);
template std::vector<double> SparseMdpPrctlHelper<double>::computeLongRunAverageRewards(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::SparseMatrix<double> const& backwardTransitions, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template double SparseMdpPrctlHelper<double>::computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template double SparseMdpPrctlHelper<double>::computeLraForMaximalEndComponentVI(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template double SparseMdpPrctlHelper<double>::computeLraForMaximalEndComponentLP(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::models::sparse::StandardRewardModel<double> const& rewardModel, storm::storage::MaximalEndComponent const& mec);
#ifdef STORM_HAVE_CARL
template class SparseMdpPrctlHelper<storm::RationalNumber>;
template std::vector<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeInstantaneousRewards(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, uint_fast64_t stepCount, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template std::vector<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeCumulativeRewards(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, uint_fast64_t stepBound, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeReachabilityRewards(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint);
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeReachabilityRewards(storm::solver::SolveGoal const& goal, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::storage::BitVector const& targetStates, bool qualitative, bool produceScheduler, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory, ModelCheckerHint const& hint);
template std::vector<storm::RationalNumber> SparseMdpPrctlHelper<storm::RationalNumber>::computeLongRunAverageRewards(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template storm::RationalNumber SparseMdpPrctlHelper<storm::RationalNumber>::computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template storm::RationalNumber SparseMdpPrctlHelper<storm::RationalNumber>::computeLraForMaximalEndComponentVI(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::storage::MaximalEndComponent const& mec, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template storm::RationalNumber SparseMdpPrctlHelper<storm::RationalNumber>::computeLraForMaximalEndComponentLP(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel, storm::storage::MaximalEndComponent const& mec);
#endif
}
}
}