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defs.mbr
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defs.mbr
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% ========================================
% PVS Types
% ========================================
type FreePVS = PVSType<mset[maxOccurrences] of 1..maxP> =
params {
array[int, 1..2] of 1..maxP: partialOrdering :: wrappedBy('java', 'isse.mbr.extensions.preprocessing.TransitiveClosure');
int: maxP;
int: maxPerSc;
int: maxOccurrences :: default('mbr.nScs * mbr.maxPerSc');
} in
instantiates with "soft_constraints/mbr_types/free-pvs-type.mzn" {
times -> multiset_union;
is_worse -> isSmythWorse;
top -> [0 | i in 1..mbr.maxP];
};
type ConstraintPreferences = PVSType<bool, set of 1..nScs> =
params {
array[int, 1..2] of 1..nScs: crEdges :: wrappedBy('java', 'isse.mbr.extensions.preprocessing.TransitiveClosure');
bool: useSPD :: default('true');
} in
instantiates with "soft_constraints/mbr_types/cr_type.mzn" {
times -> union_violateds;
is_worse -> is_worse_constraint_pref;
top -> {};
}
offers {
heuristics -> getSearchHeuristicCR;
};
type CostFunctionNetwork = PVSType<0..k> =
params {
int: k :: default('1000');
} in
instantiates with "soft_constraints/mbr_types/cfn_type.mzn" {
times -> sum;
is_worse -> is_worse_weighted;
top -> 0;
};
type MaxCostFunctionNetwork = PVSType<0..k> =
params {
int: k :: default('1000');
} in
instantiates with "soft_constraints/mbr_types/cfn_type.mzn" {
times -> sum;
is_worse -> is_greater_weighted;
top -> 0;
};
type MultiWeightedCsp = PVSType<bool, array[1..d] of 0..k> =
params {
int: k :: default('1000');
int: d :: default('1');
array[1..nScs,1..d] of int: weights :: default('1');
} in
instantiates with "soft_constraints/mbr_types/weighted_type.mzn" {
times -> weighted_multi_sum;
is_worse -> is_worse_multi_weighted;
top -> 0; % [0 | i in 1..d]
};
type WeightedCsp = PVSType<bool, int> =
params {
int: k :: default('1000');
array[1..nScs] of int: weights :: default('1');
int: amplifier :: default('1');
} in
instantiates with "soft_constraints/mbr_types/weighted_type.mzn" {
times -> weighted_sum;
is_worse -> is_worse_weighted;
top -> 0;
}
offers {
heuristics -> getSearchHeuristicWeighted;
};
type MaxCsp = PVSType<bool, int> =
params {
int: k :: default('1000');
} in
instantiates with "soft_constraints/mbr_types/weighted_type.mzn" {
times -> bool_sum;
is_worse -> is_worse_max;
top -> 0;
}
offers {
heuristics -> getSearchHeuristicMax;
};
type FuzzyConstraints = PVSType<0.0 .. 1.0> =
instantiates with "soft_constraints/mbr_types/fuzzy_type.mzn" {
times -> min;
is_worse -> is_worse_fuzzy;
top -> 1.0;
};
type ProbabilisticConstraints = PVSType<bool, 0.0 .. 1.0> =
params {
array[1..nScs] of float: probs :: default('1.0');
} in
instantiates with "soft_constraints/mbr_types/probabilistic_type.mzn" {
times -> prod;
is_worse -> is_worse_prob;
top -> 1.0;
};
type ProbCostFunctionNetwork = PVSType<float> =
params {
array[1..nScs] of float: probs :: default('1.0');
} in
instantiates with "soft_constraints/mbr_types/probabilistic_type.mzn" {
times -> prob_weighted_sum;
is_worse -> is_worse_prob_cost;
top -> k;
};
% very useful for approval voting, we give a soft acceptance or not
% similar to weighted CSP, we can have parameter defining whether
% to conjunctively or disjunctively combine soft constraint gradings
type BooleanPvs = PVSType<bool> =
params {
bool: conjunction :: default('true');
} in
instantiates with "soft_constraints/mbr_types/bool-type.mzn" {
times -> combine_boolean;
is_worse -> is_worse_boolean;
top -> true;
};
% just a simple explicit relation mostly for voting experiments
type PvsRel = PVSType<1..k> =
params {
int: k :: default('1000'); % just some upper bound on integer values
int: topVal :: default('1');
array [int,1..2] of 1..k: tuples;
} in
instantiates with "soft_constraints/mbr_types/relation-type.mzn" {
times -> max;
is_worse -> is_worse_relation;
top -> mbr.topVal;
};
% Similar to PvsRel but with an explicit approval set
type PvsApprov = PVSType<bool> =
params {
int: k :: default('1000'); % just some upper bound on integer values
set of 1..k: approvedSet;
} in
instantiates with "soft_constraints/mbr_types/relation-type.mzn" {
times -> and;
is_worse -> is_worse_boolean;
top -> true;
};
% ========================================
% MORPHISMS
% ========================================
morph ConstraintPreferences -> WeightedCsp: ToWeighted =
params {
k = 'mbr.nScs * max(i in 1..mbr.nScs) (mbr.weights[i]) ';
weights = calculate_cr_weights;
amplifier = '1';
} in id; % the "in" values denotes the function that is applied to each soft constraint (here just identity)
morph ConstraintPreferences -> WeightedCsp: ToWeightedExt =
params generatedBy('isse.mbr.extensions.weighting.SingleWeighting') {
% k = 'mbr.nScs * max(i in 1..mbr.nScs) (mbr.weights[i]) ';
k = generated ;
weights = generated ;
amplifier = '1';
} in id; % the "in" values denotes the function that is applied to each soft constraint (here just identity)
morph ConstraintPreferences -> MultiWeightedCsp: ToMultiWeighted =
params generatedBy('isse.mbr.extensions.weighting.MultiWeighting') {
k = 'mbr.nScs * max(i in 1..mbr.nScs, j in 1..mbr.d) (mbr.weights[i,j]) ';
d = generated;
weights = generated ;
} in id; % the "in" values denotes the function that is applied to each soft constraint (here just identity)
% a morphism converting a probabilistic CSP to weighted CSP using log
morph ProbabilisticConstraints -> WeightedCsp: ProbToWeighted =
params generatedBy('isse.mbr.extensions.weighting.ProbWeighting') {
k = 'mbr.nScs * max(i in 1..mbr.nScs) (mbr.weights[i]) ';
weights = generated ;
amplifier = '1';
} in id; % the "in" values denotes the function that is applied to each soft constraint (here just identity)