You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
599 lines
46 KiB
599 lines
46 KiB
/*
|
|
* SparseMdpPrctlModelChecker.h
|
|
*
|
|
* Created on: 15.02.2013
|
|
* Author: Christian Dehnert
|
|
*/
|
|
|
|
#ifndef STORM_MODELCHECKER_PRCTL_SPARSEMDPPRCTLMODELCHECKER_H_
|
|
#define STORM_MODELCHECKER_PRCTL_SPARSEMDPPRCTLMODELCHECKER_H_
|
|
|
|
#include <vector>
|
|
#include <stack>
|
|
#include <fstream>
|
|
|
|
#include "src/modelchecker/prctl/AbstractModelChecker.h"
|
|
#include "src/solver/AbstractNondeterministicLinearEquationSolver.h"
|
|
#include "src/solver/GmmxxLinearEquationSolver.h"
|
|
#include "src/models/Mdp.h"
|
|
#include "src/utility/vector.h"
|
|
#include "src/utility/graph.h"
|
|
#include "src/utility/solver.h"
|
|
#include "src/settings/Settings.h"
|
|
#include "src/storage/TotalScheduler.h"
|
|
|
|
namespace storm {
|
|
namespace modelchecker {
|
|
namespace prctl {
|
|
|
|
/*!
|
|
* This class represents the base class for all PRCTL model checkers for MDPs.
|
|
*/
|
|
template<class Type>
|
|
class SparseMdpPrctlModelChecker : public AbstractModelChecker<Type> {
|
|
|
|
public:
|
|
/*!
|
|
* Constructs a SparseMdpPrctlModelChecker with the given model.
|
|
*
|
|
* @param model The MDP to be checked.
|
|
*/
|
|
explicit SparseMdpPrctlModelChecker(storm::models::Mdp<Type> const& model) : AbstractModelChecker<Type>(model), minimumOperatorStack(), nondeterministicLinearEquationSolver(storm::utility::solver::getNondeterministicLinearEquationSolver<Type>()) {
|
|
// Intentionally left empty.
|
|
}
|
|
|
|
explicit SparseMdpPrctlModelChecker(storm::models::Mdp<Type> const& model, std::shared_ptr<storm::solver::AbstractNondeterministicLinearEquationSolver<Type>> nondeterministicLinearEquationSolver) : AbstractModelChecker<Type>(model), minimumOperatorStack(), nondeterministicLinearEquationSolver(nondeterministicLinearEquationSolver) {
|
|
// Intentionally left empty.
|
|
}
|
|
|
|
/*!
|
|
* Copy constructs a SparseMdpPrctlModelChecker from the given model checker. In particular, this means that the newly
|
|
* constructed model checker will have the model of the given model checker as its associated model.
|
|
*/
|
|
explicit SparseMdpPrctlModelChecker(storm::modelchecker::prctl::SparseMdpPrctlModelChecker<Type> const& modelchecker)
|
|
: AbstractModelChecker<Type>(modelchecker), minimumOperatorStack(), nondeterministicLinearEquationSolver(storm::utility::solver::getNondeterministicLinearEquationSolver<Type>()) {
|
|
// Intentionally left empty.
|
|
}
|
|
|
|
/*!
|
|
* Returns a constant reference to the MDP associated with this model checker.
|
|
* @returns A constant reference to the MDP associated with this model checker.
|
|
*/
|
|
storm::models::Mdp<Type> const& getModel() const {
|
|
return AbstractModelChecker<Type>::template getModel<storm::models::Mdp<Type>>();
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a P/R operator without a bound.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @returns The set of states satisfying the formula represented by a bit vector.
|
|
*/
|
|
virtual std::vector<Type> checkNoBoundOperator(const storm::property::prctl::AbstractNoBoundOperator<Type>& formula) const {
|
|
// Check if the operator was an non-optimality operator and report an error in that case.
|
|
if (!formula.isOptimalityOperator()) {
|
|
LOG4CPLUS_ERROR(logger, "Formula does not specify neither min nor max optimality, which is not meaningful over nondeterministic models.");
|
|
throw storm::exceptions::InvalidArgumentException() << "Formula does not specify neither min nor max optimality, which is not meaningful over nondeterministic models.";
|
|
}
|
|
minimumOperatorStack.push(formula.isMinimumOperator());
|
|
std::vector<Type> result = formula.check(*this, false);
|
|
minimumOperatorStack.pop();
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Computes the probability to satisfy phi until psi within a limited number of steps for each state.
|
|
*
|
|
* @param phiStates A bit vector indicating which states satisfy phi.
|
|
* @param psiStates A bit vector indicating which states satisfy psi.
|
|
* @param stepBound The upper bound for the number of steps.
|
|
* @param qualitative A flag indicating whether the check only needs to be done qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @return The probabilities for satisfying phi until psi within a limited number of steps for each state.
|
|
* If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
std::vector<Type> checkBoundedUntil(storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, uint_fast64_t stepBound, bool qualitative) const {
|
|
std::vector<Type> result(this->getModel().getNumberOfStates());
|
|
|
|
// Determine the states that have 0 probability of reaching the target states.
|
|
storm::storage::BitVector statesWithProbabilityGreater0;
|
|
if (this->minimumOperatorStack.top()) {
|
|
statesWithProbabilityGreater0 = storm::utility::graph::performProbGreater0A(this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), this->getModel().getBackwardTransitions(), phiStates, psiStates, true, stepBound);
|
|
} else {
|
|
statesWithProbabilityGreater0 = storm::utility::graph::performProbGreater0E(this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), this->getModel().getBackwardTransitions(), phiStates, psiStates, true, stepBound);
|
|
}
|
|
|
|
// Check if we already know the result (i.e. probability 0) for all initial states and
|
|
// don't compute anything in this case.
|
|
if (this->getModel().getInitialStates().isDisjointFrom(statesWithProbabilityGreater0)) {
|
|
LOG4CPLUS_INFO(logger, "The probabilities for the initial states were determined in a preprocessing step."
|
|
<< " No exact probabilities were computed.");
|
|
// Set the values for all maybe-states to 0.5 to indicate that their probability values are not 0 (and
|
|
// not necessarily 1).
|
|
storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0, Type(0.5));
|
|
} else {
|
|
// In this case we have have to compute the probabilities.
|
|
|
|
// We can eliminate the rows and columns from the original transition probability matrix that have probability 0.
|
|
storm::storage::SparseMatrix<Type> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(statesWithProbabilityGreater0, this->getModel().getNondeterministicChoiceIndices());
|
|
|
|
// Get the "new" nondeterministic choice indices for the submatrix.
|
|
std::vector<uint_fast64_t> subNondeterministicChoiceIndices = storm::utility::vector::getConstrainedOffsetVector(this->getModel().getNondeterministicChoiceIndices(), statesWithProbabilityGreater0);
|
|
|
|
// Compute the new set of target states in the reduced system.
|
|
storm::storage::BitVector rightStatesInReducedSystem = statesWithProbabilityGreater0 % psiStates;
|
|
|
|
// Make all rows absorbing that satisfy the second sub-formula.
|
|
submatrix.makeRowsAbsorbing(rightStatesInReducedSystem, subNondeterministicChoiceIndices);
|
|
|
|
// Create the vector with which to multiply.
|
|
std::vector<Type> subresult(statesWithProbabilityGreater0.getNumberOfSetBits());
|
|
storm::utility::vector::setVectorValues(subresult, rightStatesInReducedSystem, storm::utility::constGetOne<Type>());
|
|
|
|
this->nondeterministicLinearEquationSolver->performMatrixVectorMultiplication(this->minimumOperatorStack.top(), submatrix, subresult, subNondeterministicChoiceIndices, nullptr, stepBound);
|
|
|
|
// Set the values of the resulting vector accordingly.
|
|
storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0, subresult);
|
|
storm::utility::vector::setVectorValues(result, ~statesWithProbabilityGreater0, storm::utility::constGetZero<Type>());
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a bounded-until formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @return The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkBoundedUntil(storm::property::prctl::BoundedUntil<Type> const& formula, bool qualitative) const {
|
|
return checkBoundedUntil(formula.getLeft().check(*this), formula.getRight().check(*this), formula.getBound(), qualitative);
|
|
}
|
|
|
|
/*!
|
|
* Computes the probability to reach the given set of states in the next step for each state.
|
|
*
|
|
* @param nextStates A bit vector defining the states to reach in the next state.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @return The probabilities to reach the gien set of states in the next step for each state. If the
|
|
* qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkNext(storm::storage::BitVector const& nextStates, bool qualitative) const {
|
|
// Create the vector with which to multiply and initialize it correctly.
|
|
std::vector<Type> result(this->getModel().getNumberOfStates());
|
|
storm::utility::vector::setVectorValues(result, nextStates, storm::utility::constGetOne<Type>());
|
|
|
|
this->nondeterministicLinearEquationSolver->performMatrixVectorMultiplication(this->minimumOperatorStack.top(), this->getModel().getTransitionMatrix(), result, this->getModel().getNondeterministicChoiceIndices());
|
|
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a next formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @return The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkNext(const storm::property::prctl::Next<Type>& formula, bool qualitative) const {
|
|
return checkNext(formula.getChild().check(*this), qualitative);
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a bounded-eventually formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @returns The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkBoundedEventually(const storm::property::prctl::BoundedEventually<Type>& formula, bool qualitative) const {
|
|
// Create equivalent temporary bounded until formula and check it.
|
|
storm::property::prctl::BoundedUntil<Type> temporaryBoundedUntilFormula(new storm::property::prctl::Ap<Type>("true"), formula.getChild().clone(), formula.getBound());
|
|
return this->checkBoundedUntil(temporaryBoundedUntilFormula, qualitative);
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is an eventually formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @returns The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkEventually(const storm::property::prctl::Eventually<Type>& formula, bool qualitative) const {
|
|
// Create equivalent temporary until formula and check it.
|
|
storm::property::prctl::Until<Type> temporaryUntilFormula(new storm::property::prctl::Ap<Type>("true"), formula.getChild().clone());
|
|
return this->checkUntil(temporaryUntilFormula, qualitative);
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a globally formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @returns The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkGlobally(const storm::property::prctl::Globally<Type>& formula, bool qualitative) const {
|
|
// Create "equivalent" temporary eventually formula and check it.
|
|
storm::property::prctl::Eventually<Type> temporaryEventuallyFormula(new storm::property::prctl::Not<Type>(formula.getChild().clone()));
|
|
std::vector<Type> result = this->checkEventually(temporaryEventuallyFormula, qualitative);
|
|
|
|
// Now subtract the resulting vector from the constant one vector to obtain final result.
|
|
storm::utility::vector::subtractFromConstantOneVector(result);
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Check the given formula that is an until formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @param scheduler If <code>qualitative</code> is false and this vector is non-null and has as many elements as
|
|
* there are states in the MDP, this vector will represent a scheduler for the model that achieves the probability
|
|
* returned by model checking. To this end, the vector will hold the nondeterministic choice made for each state.
|
|
* @return The probabilities for the given formula to hold on every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkUntil(const storm::property::prctl::Until<Type>& formula, bool qualitative) const {
|
|
return this->checkUntil(this->minimumOperatorStack.top(), formula.getLeft().check(*this), formula.getRight().check(*this), qualitative).first;
|
|
}
|
|
|
|
/*!
|
|
* Computes the extremal probability to satisfy phi until psi for each state in the model.
|
|
*
|
|
* @param minimize If set, the probability is minimized and maximized otherwise.
|
|
* @param phiStates A bit vector indicating which states satisfy phi.
|
|
* @param psiStates A bit vector indicating which states satisfy psi.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bounds 0 and 1.
|
|
* @param scheduler If <code>qualitative</code> is false and this vector is non-null and has as many elements as
|
|
* there are states in the MDP, this vector will represent a scheduler for the model that achieves the probability
|
|
* returned by model checking. To this end, the vector will hold the nondeterministic choice made for each state.
|
|
* @return The probabilities for the satisfying phi until psi for each state of the model. If the
|
|
* qualitative flag is set, exact probabilities might not be computed.
|
|
*/
|
|
static std::pair<std::vector<Type>, storm::storage::TotalScheduler> computeUnboundedUntilProbabilities(bool minimize, storm::storage::SparseMatrix<Type> const& transitionMatrix, std::vector<uint_fast64_t> nondeterministicChoiceIndices, storm::storage::SparseMatrix<Type> const& backwardTransitions, storm::storage::BitVector const& initialStates, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, std::shared_ptr<storm::solver::AbstractNondeterministicLinearEquationSolver<Type>> nondeterministicLinearEquationSolver, bool qualitative) {
|
|
size_t numberOfStates = phiStates.size();
|
|
|
|
// We need to identify the states which have to be taken out of the matrix, i.e.
|
|
// all states that have probability 0 and 1 of satisfying the until-formula.
|
|
std::pair<storm::storage::BitVector, storm::storage::BitVector> statesWithProbability01;
|
|
if (minimize) {
|
|
statesWithProbability01 = storm::utility::graph::performProb01Min(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
|
|
} else {
|
|
statesWithProbability01 = storm::utility::graph::performProb01Max(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
|
|
}
|
|
storm::storage::BitVector statesWithProbability0 = std::move(statesWithProbability01.first);
|
|
storm::storage::BitVector statesWithProbability1 = std::move(statesWithProbability01.second);
|
|
|
|
storm::storage::BitVector maybeStates = ~(statesWithProbability0 | statesWithProbability1);
|
|
LOG4CPLUS_INFO(logger, "Found " << statesWithProbability0.getNumberOfSetBits() << " 'no' states.");
|
|
LOG4CPLUS_INFO(logger, "Found " << statesWithProbability1.getNumberOfSetBits() << " 'yes' states.");
|
|
LOG4CPLUS_INFO(logger, "Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
|
|
|
|
// Create resulting vector.
|
|
std::vector<Type> result(numberOfStates);
|
|
|
|
// Check whether we need to compute exact probabilities for some states.
|
|
if (initialStates.isDisjointFrom(maybeStates) || qualitative) {
|
|
if (qualitative) {
|
|
LOG4CPLUS_INFO(logger, "The formula was checked qualitatively. No exact probabilities were computed.");
|
|
} else {
|
|
LOG4CPLUS_INFO(logger, "The probabilities for the initial states were determined in a preprocessing step."
|
|
<< " No exact probabilities were computed.");
|
|
}
|
|
// 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<Type>(result, maybeStates, Type(0.5));
|
|
} else {
|
|
// In this case we have have to compute the probabilities.
|
|
|
|
// First, we can eliminate the rows and columns from the original transition probability matrix for states
|
|
// whose probabilities are already known.
|
|
storm::storage::SparseMatrix<Type> submatrix = transitionMatrix.getSubmatrix(maybeStates, nondeterministicChoiceIndices);
|
|
|
|
// Get the "new" nondeterministic choice indices for the submatrix.
|
|
std::vector<uint_fast64_t> subNondeterministicChoiceIndices = storm::utility::vector::getConstrainedOffsetVector(nondeterministicChoiceIndices, maybeStates);
|
|
|
|
// 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 'yes' state.
|
|
std::vector<Type> b = transitionMatrix.getConstrainedRowSumVector(maybeStates, nondeterministicChoiceIndices, statesWithProbability1, submatrix.getRowCount());
|
|
|
|
// Create vector for results for maybe states.
|
|
std::vector<Type> x(maybeStates.getNumberOfSetBits());
|
|
|
|
// Solve the corresponding system of equations.
|
|
nondeterministicLinearEquationSolver->solveEquationSystem(minimize, submatrix, x, b, subNondeterministicChoiceIndices);
|
|
|
|
// Set values of resulting vector according to result.
|
|
storm::utility::vector::setVectorValues<Type>(result, maybeStates, x);
|
|
}
|
|
|
|
// Set values of resulting vector that are known exactly.
|
|
storm::utility::vector::setVectorValues<Type>(result, statesWithProbability0, storm::utility::constGetZero<Type>());
|
|
storm::utility::vector::setVectorValues<Type>(result, statesWithProbability1, storm::utility::constGetOne<Type>());
|
|
|
|
// Finally, compute a scheduler that achieves the extramal value.
|
|
storm::storage::TotalScheduler scheduler = computeExtremalScheduler(minimize, transitionMatrix, nondeterministicChoiceIndices, result);
|
|
|
|
return std::make_pair(result, scheduler);
|
|
}
|
|
|
|
std::pair<std::vector<Type>, storm::storage::TotalScheduler> checkUntil(bool minimize, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative) const {
|
|
return computeUnboundedUntilProbabilities(minimize, this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), this->getModel().getBackwardTransitions(), this->getModel().getInitialStates(), phiStates, psiStates, this->nondeterministicLinearEquationSolver, qualitative);
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is an instantaneous reward formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bound 0. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bound 0.
|
|
* @return The reward values for the given formula for every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact values might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkInstantaneousReward(const storm::property::prctl::InstantaneousReward<Type>& formula, bool qualitative) const {
|
|
// Only compute the result if the model has a state-based reward model.
|
|
if (!this->getModel().hasStateRewards()) {
|
|
LOG4CPLUS_ERROR(logger, "Missing (state-based) reward model for formula.");
|
|
throw storm::exceptions::InvalidPropertyException() << "Missing (state-based) reward model for formula.";
|
|
}
|
|
|
|
// Initialize result to state rewards of the model.
|
|
std::vector<Type> result(this->getModel().getStateRewardVector());
|
|
|
|
this->nondeterministicLinearEquationSolver->performMatrixVectorMultiplication(this->minimumOperatorStack.top(), this->getModel().getTransitionMatrix(), result, this->getModel().getNondeterministicChoiceIndices(), nullptr, formula.getBound());
|
|
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Check the given formula that is a cumulative reward formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bound 0. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bound 0.
|
|
* @return The reward values for the given formula for every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact values might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkCumulativeReward(const storm::property::prctl::CumulativeReward<Type>& formula, bool qualitative) const {
|
|
// Only compute the result if the model has at least one reward model.
|
|
if (!this->getModel().hasStateRewards() && !this->getModel().hasTransitionRewards()) {
|
|
LOG4CPLUS_ERROR(logger, "Missing reward model for formula.");
|
|
throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
|
|
}
|
|
|
|
// Compute the reward vector to add in each step based on the available reward models.
|
|
std::vector<Type> totalRewardVector;
|
|
if (this->getModel().hasTransitionRewards()) {
|
|
totalRewardVector = this->getModel().getTransitionMatrix().getPointwiseProductRowSumVector(this->getModel().getTransitionRewardMatrix());
|
|
if (this->getModel().hasStateRewards()) {
|
|
storm::utility::vector::addVectorsInPlace(totalRewardVector, this->getModel().getStateRewardVector());
|
|
}
|
|
} else {
|
|
totalRewardVector = std::vector<Type>(this->getModel().getStateRewardVector());
|
|
}
|
|
|
|
// Initialize result to either the state rewards of the model or the null vector.
|
|
std::vector<Type> result;
|
|
if (this->getModel().hasStateRewards()) {
|
|
result = std::vector<Type>(this->getModel().getStateRewardVector());
|
|
} else {
|
|
result.resize(this->getModel().getNumberOfStates());
|
|
}
|
|
|
|
this->nondeterministicLinearEquationSolver->performMatrixVectorMultiplication(this->minimumOperatorStack.top(), this->getModel().getTransitionMatrix(), result, this->getModel().getNondeterministicChoiceIndices(), &totalRewardVector, formula.getBound());
|
|
|
|
return result;
|
|
}
|
|
|
|
/*!
|
|
* Checks the given formula that is a reachability reward formula.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bound 0. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bound 0.
|
|
* @return The reward values for the given formula for every state of the model associated with this model
|
|
* checker. If the qualitative flag is set, exact values might not be computed.
|
|
*/
|
|
virtual std::vector<Type> checkReachabilityReward(const storm::property::prctl::ReachabilityReward<Type>& formula, bool qualitative) const {
|
|
return this->checkReachabilityReward(this->minimumOperatorStack.top(), formula.getChild().check(*this), qualitative).first;
|
|
}
|
|
|
|
/*!
|
|
* Computes the expected reachability reward that is gained before a target state is reached for each state.
|
|
*
|
|
* @param minimize If set, the reward is to be minimized and maximized otherwise.
|
|
* @param targetStates The target states before which rewards can be gained.
|
|
* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
|
|
* results are only compared against the bound 0. If set to true, this will most likely results that are only
|
|
* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
|
|
* bound 0.
|
|
* @param scheduler If <code>qualitative</code> is false and this vector is non-null and has as many elements as
|
|
* there are states in the MDP, this vector will represent a scheduler for the model that achieves the probability
|
|
* returned by model checking. To this end, the vector will hold the nondeterministic choice made for each state.
|
|
* @return The expected reward values gained before a target state is reached for each state. If the
|
|
* qualitative flag is set, exact values might not be computed.
|
|
*/
|
|
virtual std::pair<std::vector<Type>, storm::storage::TotalScheduler> checkReachabilityReward(bool minimize, storm::storage::BitVector const& targetStates, bool qualitative) const {
|
|
// Only compute the result if the model has at least one reward model.
|
|
if (!(this->getModel().hasStateRewards() || this->getModel().hasTransitionRewards())) {
|
|
LOG4CPLUS_ERROR(logger, "Missing reward model for formula.");
|
|
throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
|
|
}
|
|
|
|
// Determine which states have a reward of infinity by definition.
|
|
storm::storage::BitVector infinityStates;
|
|
storm::storage::BitVector trueStates(this->getModel().getNumberOfStates(), true);
|
|
if (minimize) {
|
|
infinityStates = std::move(storm::utility::graph::performProb1A(this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), this->getModel().getBackwardTransitions(), trueStates, targetStates));
|
|
} else {
|
|
infinityStates = std::move(storm::utility::graph::performProb1E(this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), this->getModel().getBackwardTransitions(), trueStates, targetStates));
|
|
}
|
|
infinityStates.complement();
|
|
storm::storage::BitVector maybeStates = ~targetStates & ~infinityStates;
|
|
LOG4CPLUS_INFO(logger, "Found " << infinityStates.getNumberOfSetBits() << " 'infinity' states.");
|
|
LOG4CPLUS_INFO(logger, "Found " << targetStates.getNumberOfSetBits() << " 'target' states.");
|
|
LOG4CPLUS_INFO(logger, "Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
|
|
|
|
// Create resulting vector.
|
|
std::vector<Type> result(this->getModel().getNumberOfStates());
|
|
|
|
// Check whether we need to compute exact rewards for some states.
|
|
if (this->getModel().getInitialStates().isDisjointFrom(maybeStates)) {
|
|
LOG4CPLUS_INFO(logger, "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<Type>(result, maybeStates, storm::utility::constGetOne<Type>());
|
|
} else {
|
|
// In this case we have to compute the reward values for the remaining states.
|
|
|
|
// We can eliminate the rows and columns from the original transition probability matrix for states
|
|
// whose reward values are already known.
|
|
storm::storage::SparseMatrix<Type> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(maybeStates, this->getModel().getNondeterministicChoiceIndices());
|
|
|
|
// Get the "new" nondeterministic choice indices for the submatrix.
|
|
std::vector<uint_fast64_t> subNondeterministicChoiceIndices = storm::utility::vector::getConstrainedOffsetVector(this->getModel().getNondeterministicChoiceIndices(), maybeStates);
|
|
|
|
// 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 'yes' state.
|
|
std::vector<Type> b(submatrix.getRowCount());
|
|
|
|
if (this->getModel().hasTransitionRewards()) {
|
|
// If a transition-based reward model is available, we initialize the right-hand
|
|
// side to the vector resulting from summing the rows of the pointwise product
|
|
// of the transition probability matrix and the transition reward matrix.
|
|
std::vector<Type> pointwiseProductRowSumVector = this->getModel().getTransitionMatrix().getPointwiseProductRowSumVector(this->getModel().getTransitionRewardMatrix());
|
|
storm::utility::vector::selectVectorValues(b, maybeStates, this->getModel().getNondeterministicChoiceIndices(), pointwiseProductRowSumVector);
|
|
|
|
if (this->getModel().hasStateRewards()) {
|
|
// If a state-based reward model is also available, we need to add this vector
|
|
// as well. As the state reward vector contains entries not just for the states
|
|
// that we still consider (i.e. maybeStates), we need to extract these values
|
|
// first.
|
|
std::vector<Type> subStateRewards(b.size());
|
|
storm::utility::vector::selectVectorValuesRepeatedly(subStateRewards, maybeStates, this->getModel().getNondeterministicChoiceIndices(), this->getModel().getStateRewardVector());
|
|
storm::utility::vector::addVectorsInPlace(b, subStateRewards);
|
|
}
|
|
} else {
|
|
// If only a state-based reward model is available, we take this vector as the
|
|
// right-hand side. As the state reward vector contains entries not just for the
|
|
// states that we still consider (i.e. maybeStates), we need to extract these values
|
|
// first.
|
|
storm::utility::vector::selectVectorValuesRepeatedly(b, maybeStates, this->getModel().getNondeterministicChoiceIndices(), this->getModel().getStateRewardVector());
|
|
}
|
|
|
|
// Create vector for results for maybe states.
|
|
std::vector<Type> x(maybeStates.getNumberOfSetBits());
|
|
|
|
// Solve the corresponding system of equations.
|
|
this->nondeterministicLinearEquationSolver->solveEquationSystem(minimize, submatrix, x, b, subNondeterministicChoiceIndices);
|
|
|
|
// Set values of resulting vector according to result.
|
|
storm::utility::vector::setVectorValues<Type>(result, maybeStates, x);
|
|
}
|
|
|
|
// Set values of resulting vector that are known exactly.
|
|
storm::utility::vector::setVectorValues(result, targetStates, storm::utility::constGetZero<Type>());
|
|
storm::utility::vector::setVectorValues(result, infinityStates, storm::utility::constGetInfinity<Type>());
|
|
|
|
// Finally, compute a scheduler that achieves the extramal value.
|
|
storm::storage::TotalScheduler scheduler = computeExtremalScheduler(this->minimumOperatorStack.top(), this->getModel().getTransitionMatrix(), this->getModel().getNondeterministicChoiceIndices(), result, this->getModel().hasStateRewards() ? &this->getModel().getStateRewardVector() : nullptr, this->getModel().hasTransitionRewards() ? &this->getModel().getTransitionRewardMatrix() : nullptr);
|
|
|
|
return std::make_pair(result, scheduler);
|
|
}
|
|
|
|
protected:
|
|
/*!
|
|
* Computes the vector of choices that need to be made to minimize/maximize the model checking result for each state.
|
|
*
|
|
* @param minimize If set, all choices are resolved such that the solution value becomes minimal and maximal otherwise.
|
|
* @param nondeterministicResult The model checking result for nondeterministic choices of all states.
|
|
* @param takenChoices The output vector that is to store the taken choices.
|
|
* @param nondeterministicChoiceIndices The assignment of states to their nondeterministic choices in the matrix.
|
|
*/
|
|
static storm::storage::TotalScheduler computeExtremalScheduler(bool minimize, storm::storage::SparseMatrix<Type> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, std::vector<Type> const& result, std::vector<Type> const* stateRewardVector = nullptr, storm::storage::SparseMatrix<Type> const* transitionRewardMatrix = nullptr) {
|
|
std::vector<Type> temporaryResult(nondeterministicChoiceIndices.size() - 1);
|
|
std::vector<Type> nondeterministicResult(result);
|
|
storm::solver::GmmxxLinearEquationSolver<Type> solver;
|
|
solver.performMatrixVectorMultiplication(transitionMatrix, nondeterministicResult, nullptr, 1);
|
|
if (stateRewardVector != nullptr || transitionRewardMatrix != nullptr) {
|
|
std::vector<Type> totalRewardVector;
|
|
if (transitionRewardMatrix != nullptr) {
|
|
totalRewardVector = transitionMatrix.getPointwiseProductRowSumVector(*transitionRewardMatrix);
|
|
if (stateRewardVector != nullptr) {
|
|
std::vector<Type> stateRewards(totalRewardVector.size());
|
|
storm::utility::vector::selectVectorValuesRepeatedly(stateRewards, storm::storage::BitVector(stateRewardVector->size(), true), nondeterministicChoiceIndices, *stateRewardVector);
|
|
storm::utility::vector::addVectorsInPlace(totalRewardVector, stateRewards);
|
|
}
|
|
} else {
|
|
totalRewardVector.resize(nondeterministicResult.size());
|
|
storm::utility::vector::selectVectorValuesRepeatedly(totalRewardVector, storm::storage::BitVector(stateRewardVector->size(), true), nondeterministicChoiceIndices, *stateRewardVector);
|
|
}
|
|
storm::utility::vector::addVectorsInPlace(nondeterministicResult, totalRewardVector);
|
|
}
|
|
|
|
std::vector<uint_fast64_t> choices(result.size());
|
|
|
|
if (minimize) {
|
|
storm::utility::vector::reduceVectorMin(nondeterministicResult, temporaryResult, nondeterministicChoiceIndices, &choices);
|
|
} else {
|
|
storm::utility::vector::reduceVectorMax(nondeterministicResult, temporaryResult, nondeterministicChoiceIndices, &choices);
|
|
}
|
|
|
|
return storm::storage::TotalScheduler(choices);
|
|
}
|
|
|
|
/*!
|
|
* A stack used for storing whether we are currently computing min or max probabilities or rewards, respectively.
|
|
* The topmost element is true if and only if we are currently computing minimum probabilities or rewards.
|
|
*/
|
|
mutable std::stack<bool> minimumOperatorStack;
|
|
|
|
/*!
|
|
* A solver that is used for solving systems of linear equations that are the result of nondeterministic choices.
|
|
*/
|
|
std::shared_ptr<storm::solver::AbstractNondeterministicLinearEquationSolver<Type>> nondeterministicLinearEquationSolver;
|
|
};
|
|
} // namespace prctl
|
|
} // namespace modelchecker
|
|
} // namespace storm
|
|
|
|
#endif /* STORM_MODELCHECKER_PRCTL_SPARSEMDPPRCTLMODELCHECKER_H_ */
|