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some refactoring of refiner

tempestpy_adaptions
dehnert 8 years ago
parent
1a663e3ed7
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0f9c64f1a9
2 changed files with 100 additions and 146 deletions
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  1. 242
      src/storm/abstraction/MenuGameRefiner.cpp
  2. 4
      src/storm/abstraction/MenuGameRefiner.h

242
src/storm/abstraction/MenuGameRefiner.cpp
View File

@ -22,61 +22,54 @@ namespace storm {
}
template<storm::dd::DdType Type, typename ValueType>
storm::dd::Bdd<Type> pickPivotState(storm::dd::Bdd<Type> const& initialStates, storm::dd::Bdd<Type> const& transitionsMin, storm::dd::Bdd<Type> const& transitionsMax, std::set<storm::expressions::Variable> const& rowVariables, std::set<storm::expressions::Variable> const& columnVariables, storm::dd::Bdd<Type> const& pivotStates, boost::optional<QuantitativeResultMinMax<Type, ValueType>> const& quantitativeResult = boost::none) {
storm::dd::Bdd<Type> pickPivotStateWithMinimalDistance(storm::dd::Bdd<Type> const& initialStates, storm::dd::Bdd<Type> const& transitionsMin, storm::dd::Bdd<Type> const& transitionsMax, std::set<storm::expressions::Variable> const& rowVariables, std::set<storm::expressions::Variable> const& columnVariables, storm::dd::Bdd<Type> const& pivotStates, boost::optional<QuantitativeResultMinMax<Type, ValueType>> const& quantitativeResult = boost::none) {
// Perform a BFS and pick the first pivot state we encounter.
storm::dd::Bdd<Type> pivotState;
// Set up used variables.
storm::dd::Bdd<Type> frontierMin = initialStates;
storm::dd::Bdd<Type> frontierMax = initialStates;
storm::dd::Bdd<Type> frontierMinPivotStates = frontierMin && pivotStates;
storm::dd::Bdd<Type> frontierMaxPivotStates = frontierMinPivotStates;
storm::dd::Bdd<Type> frontierPivotStates = frontierMin && pivotStates;
// Check whether we have pivot states on the very first level.
uint64_t level = 0;
bool foundPivotState = !frontierMinPivotStates.isZero();
bool foundPivotState = !frontierPivotStates.isZero();
if (foundPivotState) {
pivotState = frontierMinPivotStates.existsAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state from " << frontierMinPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
return frontierPivotStates.existsAbstractRepresentative(rowVariables);
} else {
// Otherwise, we perform a simulatenous BFS in the sense that we make one step in both the min and max
// transitions and check for pivot states we encounter.
while (!foundPivotState) {
frontierMin = frontierMin.relationalProduct(transitionsMin, rowVariables, columnVariables);
frontierMax = frontierMax.relationalProduct(transitionsMax, rowVariables, columnVariables);
frontierMinPivotStates = frontierMin && pivotStates;
frontierMaxPivotStates = frontierMax && pivotStates;
if (!frontierMinPivotStates.isZero()) {
if (quantitativeResult) {
storm::dd::Add<Type, ValueType> frontierPivotStatesAdd = frontierMinPivotStates.template toAdd<ValueType>();
storm::dd::Add<Type, ValueType> diff = frontierPivotStatesAdd * quantitativeResult.get().max.values - frontierPivotStatesAdd * quantitativeResult.get().min.values;
pivotState = diff.maxAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state with difference " << diff.getMax() << " from " << (frontierMinPivotStates.getNonZeroCount() + frontierMaxPivotStates.getNonZeroCount()) << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
foundPivotState = true;
} else {
pivotState = frontierMinPivotStates.existsAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state from " << (frontierMinPivotStates.getNonZeroCount() + frontierMaxPivotStates.getNonZeroCount()) << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
foundPivotState = true;
}
} else if (!frontierMaxPivotStates.isZero()) {
frontierPivotStates = (frontierMin && pivotStates) || (frontierMax && pivotStates);
if (!frontierPivotStates.isZero()) {
if (quantitativeResult) {
storm::dd::Add<Type, ValueType> frontierPivotStatesAdd = frontierMaxPivotStates.template toAdd<ValueType>();
storm::dd::Add<Type, ValueType> frontierPivotStatesAdd = frontierPivotStates.template toAdd<ValueType>();
storm::dd::Add<Type, ValueType> diff = frontierPivotStatesAdd * quantitativeResult.get().max.values - frontierPivotStatesAdd * quantitativeResult.get().min.values;
pivotState = diff.maxAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state with difference " << diff.getMax() << " from " << (frontierMinPivotStates.getNonZeroCount() + frontierMaxPivotStates.getNonZeroCount()) << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
foundPivotState = true;
STORM_LOG_TRACE("Picked pivot state with difference " << diff.getMax() << " from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
return diff.maxAbstractRepresentative(rowVariables);
} else {
pivotState = frontierMinPivotStates.existsAbstractRepresentative(rowVariables);
STORM_LOG_TRACE("Picked pivot state from " << (frontierMinPivotStates.getNonZeroCount() + frontierMaxPivotStates.getNonZeroCount()) << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
foundPivotState = true;
STORM_LOG_TRACE("Picked pivot state from " << frontierPivotStates.getNonZeroCount() << " candidates on level " << level << ", " << pivotStates.getNonZeroCount() << " candidates in total.");
return frontierPivotStates.existsAbstractRepresentative(rowVariables);
}
}
++level;
}
}
return pivotState;
STORM_LOG_ASSERT(false, "This point must not be reached, because then no pivot state could be found.");
return storm::dd::Bdd<Type>();
}
template <storm::dd::DdType Type, typename ValueType>
void MenuGameRefiner<Type, ValueType>::refine(storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& player1Choice, storm::dd::Bdd<Type> const& lowerChoice, storm::dd::Bdd<Type> const& upperChoice) const {
storm::expressions::Expression MenuGameRefiner<Type, ValueType>::derivePredicateFromDifferingChoices(storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& player1Choice, storm::dd::Bdd<Type> const& lowerChoice, storm::dd::Bdd<Type> const& upperChoice) const {
// Prepare result.
storm::expressions::Expression newPredicate;
// Get abstraction informatin for easier access.
AbstractionInformation<Type> const& abstractionInformation = abstractor.get().getAbstractionInformation();
// Decode the index of the command chosen by player 1.
@ -92,9 +85,8 @@ namespace storm {
// command (that is the player 1 choice).
if (buttomStateSuccessor) {
STORM_LOG_TRACE("One of the successors is a bottom state, taking a guard as a new predicate.");
storm::expressions::Expression newPredicate = abstractor.get().getGuard(player1Index);
STORM_LOG_DEBUG("Derived new predicate: " << newPredicate);
this->performRefinement({newPredicate});
newPredicate = abstractor.get().getGuard(player1Index);
STORM_LOG_DEBUG("Derived new predicate (based on guard): " << newPredicate);
} else {
STORM_LOG_TRACE("No bottom state successor. Deriving a new predicate using weakest precondition.");
@ -104,7 +96,6 @@ namespace storm {
STORM_LOG_ASSERT(lowerChoiceUpdateToSuccessorMapping.size() == upperChoiceUpdateToSuccessorMapping.size(), "Mismatching sizes after decode (" << lowerChoiceUpdateToSuccessorMapping.size() << " vs. " << upperChoiceUpdateToSuccessorMapping.size() << ").");
// Now go through the mappings and find points of deviation. Currently, we take the first deviation.
storm::expressions::Expression newPredicate;
auto lowerIt = lowerChoiceUpdateToSuccessorMapping.begin();
auto lowerIte = lowerChoiceUpdateToSuccessorMapping.end();
auto upperIt = upperChoiceUpdateToSuccessorMapping.begin();
@ -123,43 +114,36 @@ namespace storm {
}
}
STORM_LOG_ASSERT(newPredicate.isInitialized(), "Could not derive new predicate as there is no deviation.");
STORM_LOG_DEBUG("Derived new predicate: " << newPredicate);
this->performRefinement({newPredicate});
STORM_LOG_DEBUG("Derived new predicate (based on weakest-precondition): " << newPredicate);
}
STORM_LOG_TRACE("Current set of predicates:");
for (auto const& predicate : abstractionInformation.getPredicates()) {
STORM_LOG_TRACE(predicate);
}
return newPredicate;
}
template<storm::dd::DdType Type>
struct PivotStateResult {
storm::dd::Bdd<Type> reachableTransitionsMin;
storm::dd::Bdd<Type> reachableTransitionsMax;
storm::dd::Bdd<Type> pivotStates;
};
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) const {
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();
PivotStateResult<Type> computePivotStates(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& transitionMatrixBdd, storm::dd::Bdd<Type> const& minPlayer1Strategy, storm::dd::Bdd<Type> const& minPlayer2Strategy, storm::dd::Bdd<Type> const& maxPlayer1Strategy, storm::dd::Bdd<Type> const& maxPlayer2Strategy) {
// 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);
PivotStateResult<Type> result;
// Build the fragment of transitions that is reachable by either the min or the max strategies.
storm::dd::Bdd<Type> reachableTransitions = transitionMatrixBdd && (minPlayer1Strategy || maxPlayer1Strategy) && minPlayer2Strategy && maxPlayer2Strategy;
reachableTransitions = reachableTransitions.existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> reachableTransitionsMin = (transitionMatrixBdd && minPlayer1Strategy && minPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> reachableTransitionsMax = (transitionMatrixBdd && maxPlayer1Strategy && maxPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
result.reachableTransitionsMin = (transitionMatrixBdd && minPlayer1Strategy && minPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
result.reachableTransitionsMax = (transitionMatrixBdd && maxPlayer1Strategy && maxPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
// Start with all reachable states as potential pivot states.
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitionsMin, game.getRowVariables(), game.getColumnVariables()) ||
storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables());
//storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
result.pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), result.reachableTransitionsMin, game.getRowVariables(), game.getColumnVariables()) ||
storm::utility::dd::computeReachableStates(game.getInitialStates(), result.reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables());
// 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.
@ -170,22 +154,13 @@ namespace storm {
constraint &= minPlayer2Strategy.exclusiveOr(maxPlayer2Strategy);
// Then restrict the pivot states by requiring existing and different player 2 choices.
// pivotStates &= ((minPlayer1Strategy || maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
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 prob1 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, ValueType>(game.getInitialStates(), reachableTransitionsMin, reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables(), pivotStates);
result.pivotStates &= ((minPlayer1Strategy && maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
return result;
}
template<storm::dd::DdType Type, typename ValueType>
storm::expressions::Expression MenuGameRefiner<Type, ValueType>::derivePredicateFromPivotState(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& minPlayer1Strategy, storm::dd::Bdd<Type> const& minPlayer2Strategy, storm::dd::Bdd<Type> const& maxPlayer1Strategy, storm::dd::Bdd<Type> const& maxPlayer2Strategy) const {
// 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());
@ -198,10 +173,10 @@ namespace storm {
STORM_LOG_TRACE("Refining based on lower choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
this->refine(pivotState, (pivotState && minPlayer1Strategy).existsAbstract(game.getRowVariables()), lowerChoice1, lowerChoice2);
storm::expressions::Expression newPredicate = derivePredicateFromDifferingChoices(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;
return newPredicate;
} else {
storm::dd::Bdd<Type> upperChoice = pivotState && game.getExtendedTransitionMatrix().toBdd() && maxPlayer1Strategy;
storm::dd::Bdd<Type> upperChoice1 = (upperChoice && minPlayer2Strategy).existsAbstract(variablesToAbstract);
@ -211,15 +186,48 @@ namespace storm {
if (upperChoicesDifferent) {
STORM_LOG_TRACE("Refining based on upper choice.");
auto refinementStart = std::chrono::high_resolution_clock::now();
this->refine(pivotState, (pivotState && maxPlayer1Strategy).existsAbstract(game.getRowVariables()), upperChoice1, upperChoice2);
storm::expressions::Expression newPredicate = derivePredicateFromDifferingChoices(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;
return newPredicate;
} 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, QualitativeResultMinMax<Type> const& qualitativeResult) const {
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);
// Compute all reached pivot states.
PivotStateResult<Type> pivotStateResult = computePivotStates(game, transitionMatrixBdd, minPlayer1Strategy, minPlayer2Strategy, maxPlayer1Strategy, maxPlayer2Strategy);
// 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 prob1 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 (pivotStateResult.pivotStates.isZero()) {
return false;
}
STORM_LOG_ASSERT(!pivotStateResult.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 = pickPivotStateWithMinimalDistance<Type, ValueType>(game.getInitialStates(), pivotStateResult.reachableTransitionsMin, pivotStateResult.reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables(), pivotStateResult.pivotStates);
// Derive predicate based on the selected pivot state.
storm::expressions::Expression newPredicate = derivePredicateFromPivotState(game, pivotState, minPlayer1Strategy, minPlayer2Strategy, maxPlayer1Strategy, maxPlayer2Strategy);
performRefinement({newPredicate});
return true;
}
template<storm::dd::DdType Type, typename ValueType>
@ -231,75 +239,21 @@ namespace storm {
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> reachableTransitionsMin = (transitionMatrixBdd && minPlayer1Strategy && minPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
storm::dd::Bdd<Type> reachableTransitionsMax = (transitionMatrixBdd && maxPlayer1Strategy && maxPlayer2Strategy).existsAbstract(game.getNondeterminismVariables());
// Start with all reachable states as potential pivot states.
// storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitions, game.getRowVariables(), game.getColumnVariables());
storm::dd::Bdd<Type> pivotStates = storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitionsMin, game.getRowVariables(), game.getColumnVariables()) ||
storm::utility::dd::computeReachableStates(game.getInitialStates(), reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables());
// Compute all reached pivot states.
PivotStateResult<Type> pivotStateResult = computePivotStates(game, transitionMatrixBdd, minPlayer1Strategy, minPlayer2Strategy, maxPlayer1Strategy, maxPlayer2Strategy);
// TODO: required?
// 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());
pivotStates &= ((minPlayer1Strategy && maxPlayer1Strategy) && constraint).existsAbstract(game.getNondeterminismVariables());
STORM_LOG_ASSERT(!pivotStates.isZero(), "Unable to refine without pivot state candidates.");
pivotStateResult.pivotStates &= quantitativeResult.min.values.less(quantitativeResult.max.values);
STORM_LOG_ASSERT(!pivotStateResult.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, ValueType>(game.getInitialStates(), reachableTransitionsMin, reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables(), pivotStates, quantitativeResult);
// 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();
this->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();
this->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.");
}
}
storm::dd::Bdd<Type> pivotState = pickPivotStateWithMinimalDistance<Type, ValueType>(game.getInitialStates(), pivotStateResult.reachableTransitionsMin, pivotStateResult.reachableTransitionsMax, game.getRowVariables(), game.getColumnVariables(), pivotStateResult.pivotStates);
// Derive predicate based on the selected pivot state.
storm::expressions::Expression newPredicate = derivePredicateFromPivotState(game, pivotState, minPlayer1Strategy, minPlayer2Strategy, maxPlayer1Strategy, maxPlayer2Strategy);
performRefinement({newPredicate});
return true;
}

4
src/storm/abstraction/MenuGameRefiner.h
View File

@ -52,8 +52,8 @@ namespace storm {
bool refine(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& transitionMatrixBdd, QuantitativeResultMinMax<Type, ValueType> const& quantitativeResult) const;
private:
void refine(storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& player1Choice, storm::dd::Bdd<Type> const& lowerChoice, storm::dd::Bdd<Type> const& upperChoice) const;
storm::expressions::Expression derivePredicateFromDifferingChoices(storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& player1Choice, storm::dd::Bdd<Type> const& lowerChoice, storm::dd::Bdd<Type> const& upperChoice) const;
storm::expressions::Expression derivePredicateFromPivotState(storm::abstraction::MenuGame<Type, ValueType> const& game, storm::dd::Bdd<Type> const& pivotState, storm::dd::Bdd<Type> const& minPlayer1Strategy, storm::dd::Bdd<Type> const& minPlayer2Strategy, storm::dd::Bdd<Type> const& maxPlayer1Strategy, storm::dd::Bdd<Type> const& maxPlayer2Strategy) const;
/*!
* Takes the given predicates, preprocesses them and then refines the abstractor.
*/

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