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moved parts of refine functionality from model checker to refiner class

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dehnert 9 years ago
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2 changed files with 189 additions and 188 deletions
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  1. 187
      src/storm/abstraction/MenuGameRefiner.cpp
  2. 190
      src/storm/modelchecker/abstraction/GameBasedMdpModelChecker.cpp

187
src/storm/abstraction/MenuGameRefiner.cpp
View File

@ -2,6 +2,8 @@
#include "storm/abstraction/MenuGameAbstractor.h"
#include "storm/utility/dd.h"
namespace storm {
namespace abstraction {
@ -15,6 +17,191 @@ namespace storm {
abstractor.get().refine(predicates);
}
template<storm::dd::DdType Type>
storm::dd::Bdd<Type> pickPivotState(storm::dd::Bdd<Type> const& initialStates, storm::dd::Bdd<Type> const& transitions, std::set<storm::expressions::Variable> const& rowVariables, std::set<storm::expressions::Variable> const& columnVariables, storm::dd::Bdd<Type> const& pivotStates) {
// Perform a BFS and pick the first pivot state we encounter.
storm::dd::Bdd<Type> pivotState;
storm::dd::Bdd<Type> frontier = initialStates;
storm::dd::Bdd<Type> frontierPivotStates = frontier && pivotStates;
uint64_t level = 0;
bool foundPivotState = !frontierPivotStates.isZero();
if (foundPivotState) {
pivotState = frontierPivotStates.existsAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
} else {
while (!foundPivotState) {
frontier = frontier.relationalProduct(transitions, rowVariables, columnVariables);
frontierPivotStates = frontier && pivotStates;
if (!frontierPivotStates.isZero()) {
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
pivotState = frontierPivotStates.existsAbstractRepresentative(rowVariables);
foundPivotState = true;
}
++level;
}
}
return pivotState;
}
template<storm::dd::DdType Type, typename ValueType>
bool MenuGameRefiner<Type, ValueType>::refine(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& transitionMatrixBdd, QualitativeResultMinMax<Type> const& qualitativeResult) {
STORM_LOG_TRACE("Trying refinement after qualitative check.");
// Get all relevant strategies.
storm::dd::Bdd<Type> minPlayer1Strategy = qualitativeResult.prob0Min.getPlayer1Strategy();
storm::dd::Bdd<Type> minPlayer2Strategy = qualitativeResult.prob0Min.getPlayer2Strategy();
storm::dd::Bdd<Type> maxPlayer1Strategy = qualitativeResult.prob1Max.getPlayer1Strategy();
storm::dd::Bdd<Type> maxPlayer2Strategy = qualitativeResult.prob1Max.getPlayer2Strategy();
// Redirect all player 1 choices of the min strategy to that of the max strategy if this leads to a player 2
// state that is also a prob 0 state.
minPlayer1Strategy = (maxPlayer1Strategy && qualitativeResult.prob0Min.getPlayer2States()).existsAbstract(game.getPlayer1Variables()).ite(maxPlayer1Strategy, minPlayer1Strategy);
// Build the fragment of transitions that is reachable by both the min and the max strategies.
storm::dd::Bdd<Type> reachableTransitions = transitionMatrixBdd && (minPlayer1Strategy || minPlayer2Strategy) && maxPlayer1Strategy && maxPlayer2Strategy;
reachableTransitions = reachableTransitions.existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
// Require the pivot state to be a [0, 1] state.
// TODO: is this restriction necessary or is it already implied?
// pivotStates &= prob01.min.first.getPlayer1States() && prob01.max.second.getPlayer1States();
// Then constrain these states by the requirement that for either the lower or upper player 1 choice the player 2 choices must be different and
// that the difference is not because of a missing strategy in either case.
// Start with constructing the player 2 states that have a prob 0 (min) and prob 1 (max) strategy.
storm::dd::Bdd<Type> constraint = minPlayer2Strategy.existsAbstract(game.getPlayer2Variables()) && maxPlayer2Strategy.existsAbstract(game.getPlayer2Variables());
// Now construct all player 2 choices that actually exist and differ in the min and max case.
constraint &= minPlayer2Strategy.exclusiveOr(maxPlayer2Strategy);
// Then restrict the pivot states by requiring existing and different player 2 choices.
pivotStates &= ((minPlayer1Strategy || maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
// We can only refine in case we have a reachable player 1 state with a player 2 successor (under either
// player 1's min or max strategy) such that from this player 2 state, both prob0 min and prob0 max define
// strategies and they differ. Hence, it is possible that we arrive at a point where no suitable pivot state
// is found. In this case, we abort the qualitative refinement here.
if (pivotStates.isZero()) {
return false;
}
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to proceed without pivot state candidates.");
// Now that we have the pivot state candidates, we need to pick one.
storm::dd::Bdd<Type> pivotState = pickPivotState<Type>(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables(), pivotStates);
// Compute the lower and the upper choice for the pivot state.
std::set<storm::expressions::Variable> variablesToAbstract = game.getNondeterminismVariables();
variablesToAbstract.insert(game.getRowVariables().begin(), game.getRowVariables().end());
storm::dd::Bdd<Type> lowerChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && minPlayer1Strategy;
storm::dd::Bdd<Type> lowerChoice1 = (lowerChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> lowerChoice2 = (lowerChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool lowerChoicesDifferent = !lowerChoice1.exclusiveOr(lowerChoice2).isZero();
if (lowerChoicesDifferent) {
STORM_LOG_TRACE("Refining based on lower choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.get().refine(pivotState, (pivotState && minPlayer1Strategy).existsAbstract(game.getRowVariables()), lowerChoice1, lowerChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
return true;
} else {
storm::dd::Bdd<Type> upperChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && maxPlayer1Strategy;
storm::dd::Bdd<Type> upperChoice1 = (upperChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> upperChoice2 = (upperChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool upperChoicesDifferent = !upperChoice1.exclusiveOr(upperChoice2).isZero();
if (upperChoicesDifferent) {
STORM_LOG_TRACE("Refining based on upper choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.get().refine(pivotState, (pivotState && maxPlayer1Strategy).existsAbstract(game.getRowVariables()), upperChoice1, upperChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
return true;
} else {
STORM_LOG_ASSERT(false, "Did not find choices from which to derive predicates.");
}
}
return false;
}
template<storm::dd::DdType Type, typename ValueType>
bool MenuGameRefiner<Type, ValueType>::refine(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& transitionMatrixBdd, QuantitativeResultMinMax<Type, ValueType> const& quantitativeResult) {
STORM_LOG_TRACE("Refining after quantitative check.");
// Get all relevant strategies.
storm::dd::Bdd<Type> minPlayer1Strategy = quantitativeResult.min.player1Strategy;
storm::dd::Bdd<Type> minPlayer2Strategy = quantitativeResult.min.player2Strategy;
storm::dd::Bdd<Type> maxPlayer1Strategy = quantitativeResult.max.player1Strategy;
storm::dd::Bdd<Type> maxPlayer2Strategy = quantitativeResult.max.player2Strategy;
// TODO: fix min strategies to take the max strategies if possible.
// Build the fragment of transitions that is reachable by both the min and the max strategies.
storm::dd::Bdd<Type> reachableTransitions = transitionMatrixBdd && (minPlayer1Strategy || maxPlayer1Strategy) && minPlayer2Strategy && maxPlayer2Strategy;
reachableTransitions = reachableTransitions.existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
// Require the pivot state to be a state with a lower bound strictly smaller than the upper bound.
pivotStates &= quantitativeResult.min.values.less(quantitativeResult.max.values);
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to refine without pivot state candidates.");
// Then constrain these states by the requirement that for either the lower or upper player 1 choice the player 2 choices must be different and
// that the difference is not because of a missing strategy in either case.
// Start with constructing the player 2 states that have a (min) and a (max) strategy.
// TODO: necessary?
storm::dd::Bdd<Type> constraint = minPlayer2Strategy.existsAbstract(game.getPlayer2Variables()) && maxPlayer2Strategy.existsAbstract(game.getPlayer2Variables());
// Now construct all player 2 choices that actually exist and differ in the min and max case.
constraint &= minPlayer2Strategy.exclusiveOr(maxPlayer2Strategy);
// Then restrict the pivot states by requiring existing and different player 2 choices.
pivotStates &= ((minPlayer1Strategy || maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to refine without pivot state candidates.");
// Now that we have the pivot state candidates, we need to pick one.
storm::dd::Bdd<Type> pivotState = pickPivotState<Type>(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables(), pivotStates);
// Compute the lower and the upper choice for the pivot state.
std::set<storm::expressions::Variable> variablesToAbstract = game.getNondeterminismVariables();
variablesToAbstract.insert(game.getRowVariables().begin(), game.getRowVariables().end());
storm::dd::Bdd<Type> lowerChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && minPlayer1Strategy;
storm::dd::Bdd<Type> lowerChoice1 = (lowerChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> lowerChoice2 = (lowerChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool lowerChoicesDifferent = !lowerChoice1.exclusiveOr(lowerChoice2).isZero();
if (lowerChoicesDifferent) {
STORM_LOG_TRACE("Refining based on lower choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.get().refine(pivotState, (pivotState && minPlayer1Strategy).existsAbstract(game.getRowVariables()), lowerChoice1, lowerChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
} else {
storm::dd::Bdd<Type> upperChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && maxPlayer1Strategy;
storm::dd::Bdd<Type> upperChoice1 = (upperChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> upperChoice2 = (upperChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool upperChoicesDifferent = !upperChoice1.exclusiveOr(upperChoice2).isZero();
if (upperChoicesDifferent) {
STORM_LOG_TRACE("Refining based on upper choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.get().refine(pivotState, (pivotState && maxPlayer1Strategy).existsAbstract(game.getRowVariables()), upperChoice1, upperChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
} else {
STORM_LOG_ASSERT(false, "Did not find choices from which to derive predicates.");
}
}
}
template class MenuGameRefiner<storm::dd::DdType::CUDD, double>;
template class MenuGameRefiner<storm::dd::DdType::Sylvan, double>;

190
src/storm/modelchecker/abstraction/GameBasedMdpModelChecker.cpp
View File

@ -19,7 +19,6 @@
#include "storm/solver/SymbolicGameSolver.h"
#include "storm/utility/solver.h"
#include "storm/utility/dd.h"
#include "storm/utility/prism.h"
#include "storm/utility/macros.h"
@ -177,191 +176,6 @@ namespace storm {
return result;
}
template<storm::dd::DdType Type>
storm::dd::Bdd<Type> pickPivotState(storm::dd::Bdd<Type> const& initialStates, storm::dd::Bdd<Type> const& transitions, std::set<storm::expressions::Variable> const& rowVariables, std::set<storm::expressions::Variable> const& columnVariables, storm::dd::Bdd<Type> const& pivotStates) {
// Perform a BFS and pick the first pivot state we encounter.
storm::dd::Bdd<Type> pivotState;
storm::dd::Bdd<Type> frontier = initialStates;
storm::dd::Bdd<Type> frontierPivotStates = frontier && pivotStates;
uint64_t level = 0;
bool foundPivotState = !frontierPivotStates.isZero();
if (foundPivotState) {
pivotState = frontierPivotStates.existsAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
} else {
while (!foundPivotState) {
frontier = frontier.relationalProduct(transitions, rowVariables, columnVariables);
frontierPivotStates = frontier && pivotStates;
if (!frontierPivotStates.isZero()) {
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
pivotState = frontierPivotStates.existsAbstractRepresentative(rowVariables);
foundPivotState = true;
}
++level;
}
}
return pivotState;
}
template<storm::dd::DdType Type, typename ValueType>
bool refineAfterQualitativeCheck(storm::abstraction::prism::PrismMenuGameAbstractor<Type, ValueType>& abstractor, storm::abstraction::MenuGame<Type, ValueType> const& game, QualitativeResultMinMax<Type> const& qualitativeResult, storm::dd::Bdd<Type> const& transitionMatrixBdd) {
STORM_LOG_TRACE("Trying refinement after qualitative check.");
// Get all relevant strategies.
storm::dd::Bdd<Type> minPlayer1Strategy = qualitativeResult.prob0Min.getPlayer1Strategy();
storm::dd::Bdd<Type> minPlayer2Strategy = qualitativeResult.prob0Min.getPlayer2Strategy();
storm::dd::Bdd<Type> maxPlayer1Strategy = qualitativeResult.prob1Max.getPlayer1Strategy();
storm::dd::Bdd<Type> maxPlayer2Strategy = qualitativeResult.prob1Max.getPlayer2Strategy();
// Redirect all player 1 choices of the min strategy to that of the max strategy if this leads to a player 2
// state that is also a prob 0 state.
minPlayer1Strategy = (maxPlayer1Strategy && qualitativeResult.prob0Min.getPlayer2States()).existsAbstract(game.getPlayer1Variables()).ite(maxPlayer1Strategy, minPlayer1Strategy);
// Build the fragment of transitions that is reachable by both the min and the max strategies.
storm::dd::Bdd<Type> reachableTransitions = transitionMatrixBdd && (minPlayer1Strategy || minPlayer2Strategy) && maxPlayer1Strategy && maxPlayer2Strategy;
reachableTransitions = reachableTransitions.existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
// Require the pivot state to be a [0, 1] state.
// TODO: is this restriction necessary or is it already implied?
// pivotStates &= prob01.min.first.getPlayer1States() && prob01.max.second.getPlayer1States();
// Then constrain these states by the requirement that for either the lower or upper player 1 choice the player 2 choices must be different and
// that the difference is not because of a missing strategy in either case.
// Start with constructing the player 2 states that have a prob 0 (min) and prob 1 (max) strategy.
storm::dd::Bdd<Type> constraint = minPlayer2Strategy.existsAbstract(game.getPlayer2Variables()) && maxPlayer2Strategy.existsAbstract(game.getPlayer2Variables());
// Now construct all player 2 choices that actually exist and differ in the min and max case.
constraint &= minPlayer2Strategy.exclusiveOr(maxPlayer2Strategy);
// Then restrict the pivot states by requiring existing and different player 2 choices.
pivotStates &= ((minPlayer1Strategy || maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
// We can only refine in case we have a reachable player 1 state with a player 2 successor (under either
// player 1's min or max strategy) such that from this player 2 state, both prob0 min and prob0 max define
// strategies and they differ. Hence, it is possible that we arrive at a point where no suitable pivot state
// is found. In this case, we abort the qualitative refinement here.
if (pivotStates.isZero()) {
return false;
}
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to proceed without pivot state candidates.");
// Now that we have the pivot state candidates, we need to pick one.
storm::dd::Bdd<Type> pivotState = pickPivotState<Type>(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables(), pivotStates);
// Compute the lower and the upper choice for the pivot state.
std::set<storm::expressions::Variable> variablesToAbstract = game.getNondeterminismVariables();
variablesToAbstract.insert(game.getRowVariables().begin(), game.getRowVariables().end());
storm::dd::Bdd<Type> lowerChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && minPlayer1Strategy;
storm::dd::Bdd<Type> lowerChoice1 = (lowerChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> lowerChoice2 = (lowerChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool lowerChoicesDifferent = !lowerChoice1.exclusiveOr(lowerChoice2).isZero();
if (lowerChoicesDifferent) {
STORM_LOG_TRACE("Refining based on lower choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.refine(pivotState, (pivotState && minPlayer1Strategy).existsAbstract(game.getRowVariables()), lowerChoice1, lowerChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
return true;
} else {
storm::dd::Bdd<Type> upperChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && maxPlayer1Strategy;
storm::dd::Bdd<Type> upperChoice1 = (upperChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> upperChoice2 = (upperChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool upperChoicesDifferent = !upperChoice1.exclusiveOr(upperChoice2).isZero();
if (upperChoicesDifferent) {
STORM_LOG_TRACE("Refining based on upper choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.refine(pivotState, (pivotState && maxPlayer1Strategy).existsAbstract(game.getRowVariables()), upperChoice1, upperChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
return true;
} else {
STORM_LOG_ASSERT(false, "Did not find choices from which to derive predicates.");
}
}
return false;
}
template<storm::dd::DdType Type, typename ValueType>
void refineAfterQuantitativeCheck(storm::abstraction::prism::PrismMenuGameAbstractor<Type, ValueType>& abstractor, storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& transitionMatrixBdd, QuantitativeResultMinMax<Type, ValueType> const& quantitativeResult) {
STORM_LOG_TRACE("Refining after quantitative check.");
// Get all relevant strategies.
storm::dd::Bdd<Type> minPlayer1Strategy = quantitativeResult.min.player1Strategy;
storm::dd::Bdd<Type> minPlayer2Strategy = quantitativeResult.min.player2Strategy;
storm::dd::Bdd<Type> maxPlayer1Strategy = quantitativeResult.max.player1Strategy;
storm::dd::Bdd<Type> maxPlayer2Strategy = quantitativeResult.max.player2Strategy;
// TODO: fix min strategies to take the max strategies if possible.
// Build the fragment of transitions that is reachable by both the min and the max strategies.
storm::dd::Bdd<Type> reachableTransitions = transitionMatrixBdd && (minPlayer1Strategy || maxPlayer1Strategy) && minPlayer2Strategy && maxPlayer2Strategy;
reachableTransitions = reachableTransitions.existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
// Require the pivot state to be a state with a lower bound strictly smaller than the upper bound.
pivotStates &= quantitativeResult.min.values.less(quantitativeResult.max.values);
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to refine without pivot state candidates.");
// Then constrain these states by the requirement that for either the lower or upper player 1 choice the player 2 choices must be different and
// that the difference is not because of a missing strategy in either case.
// Start with constructing the player 2 states that have a (min) and a (max) strategy.
// TODO: necessary?
storm::dd::Bdd<Type> constraint = minPlayer2Strategy.existsAbstract(game.getPlayer2Variables()) && maxPlayer2Strategy.existsAbstract(game.getPlayer2Variables());
// Now construct all player 2 choices that actually exist and differ in the min and max case.
constraint &= minPlayer2Strategy.exclusiveOr(maxPlayer2Strategy);
// Then restrict the pivot states by requiring existing and different player 2 choices.
pivotStates &= ((minPlayer1Strategy || maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to refine without pivot state candidates.");
// Now that we have the pivot state candidates, we need to pick one.
storm::dd::Bdd<Type> pivotState = pickPivotState<Type>(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables(), pivotStates);
// Compute the lower and the upper choice for the pivot state.
std::set<storm::expressions::Variable> variablesToAbstract = game.getNondeterminismVariables();
variablesToAbstract.insert(game.getRowVariables().begin(), game.getRowVariables().end());
storm::dd::Bdd<Type> lowerChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && minPlayer1Strategy;
storm::dd::Bdd<Type> lowerChoice1 = (lowerChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> lowerChoice2 = (lowerChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool lowerChoicesDifferent = !lowerChoice1.exclusiveOr(lowerChoice2).isZero();
if (lowerChoicesDifferent) {
STORM_LOG_TRACE("Refining based on lower choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.refine(pivotState, (pivotState && minPlayer1Strategy).existsAbstract(game.getRowVariables()), lowerChoice1, lowerChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
} else {
storm::dd::Bdd<Type> upperChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && maxPlayer1Strategy;
storm::dd::Bdd<Type> upperChoice1 = (upperChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
storm::dd::Bdd<Type> upperChoice2 = (upperChoice && maxPlayer2Strategy).existsAbstract(variablesToAbstract);
bool upperChoicesDifferent = !upperChoice1.exclusiveOr(upperChoice2).isZero();
if (upperChoicesDifferent) {
STORM_LOG_TRACE("Refining based on upper choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
abstractor.refine(pivotState, (pivotState && maxPlayer1Strategy).existsAbstract(game.getRowVariables()), upperChoice1, upperChoice2);
auto refinementEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_TRACE("Refinement completed in " << std::chrono::duration_cast<std::chrono::milliseconds>(refinementEnd - refinementStart).count() << "ms.");
} else {
STORM_LOG_ASSERT(false, "Did not find choices from which to derive predicates.");
}
}
}
template<storm::dd::DdType Type, typename ValueType>
QuantitativeResult<Type, ValueType> solveMaybeStates(storm::OptimizationDirection const& player1Direction, storm::OptimizationDirection const& player2Direction, storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& maybeStates, storm::dd::Bdd<Type> const& prob1States, boost::optional<QuantitativeResult<Type, ValueType>> const& startInfo = boost::none) {
@ -516,7 +330,7 @@ namespace storm {
// If we get here, the initial states were all identified as prob0/1 states, but the value (0 or 1)
// depends on whether player 2 is minimizing or maximizing. Therefore, we need to find a place to refine.
qualitativeRefinement = refineAfterQualitativeCheck(abstractor, game, qualitativeResult, transitionMatrixBdd);
qualitativeRefinement = refiner.refine(game, transitionMatrixBdd, qualitativeResult);
}
}
@ -562,7 +376,7 @@ namespace storm {
// (10) If we arrived at this point, it means that we have all qualitative and quantitative
// information about the game, but we could not yet answer the query. In this case, we need to refine.
refineAfterQuantitativeCheck(abstractor, game, transitionMatrixBdd, quantitativeResult);
refiner.refine(game, transitionMatrixBdd, quantitativeResult);
}
auto iterationEnd = std::chrono::high_resolution_clock::now();
STORM_LOG_DEBUG("Iteration " << iterations << " took " << std::chrono::duration_cast<std::chrono::milliseconds>(iterationEnd - iterationStart).count() << "ms.");

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