Package de.learnlib.algorithm

Interface LearningAlgorithm<M,I,D>

Type Parameters:

M - model type

I - input symbol type

D - output domain type

All Known Subinterfaces:

GlobalSuffixLearner<M,I,D>, GlobalSuffixLearner.GlobalSuffixLearnerDFA<I>, GlobalSuffixLearner.GlobalSuffixLearnerMealy<I,O>, LearningAlgorithm.DFALearner<I>, LearningAlgorithm.MealyLearner<I,O>, LearningAlgorithm.MooreLearner<I,O>, LearningAlgorithm.NFALearner<I>, OTLearner<M,I,D>, OTLearner.OTLearnerDFA<I>, OTLearner.OTLearnerMealy<I,O>, OTLearner.OTLearnerMoore<I,O>, StatisticLearner<M,I,D>, StatisticLearner.DFAStatisticLearner<I>, StatisticLearner.MealyStatisticLearner<I,O>, StatisticLearner.MooreStatisticLearner<I,O>

All Known Implementing Classes:

AbstractAAARLearner, AbstractAutomatonLStar, AbstractExplicitAAARLearner, AbstractExtensibleAutomatonLStar, AbstractGenericAAARLearner, AbstractLStar, AbstractOPLearner, AbstractOptimalTTT, AbstractTTTLearner, AbstractVPALearner, ADTLearner, ClassicLStarDFA, ClassicLStarMealy, ClassicLStarMoore, DFARefinementCounterLearner, ExplicitAAARLearnerDFA, ExplicitAAARLearnerMealy, ExplicitAAARLearnerMoore, ExtensibleLStarDFA, ExtensibleLStarMealy, ExtensibleLStarMoore, GenericAAARLearnerDFA, GenericAAARLearnerMealy, GenericAAARLearnerMoore, KearnsVaziraniAdapterDFA, KearnsVaziraniAdapterMealy, KearnsVaziraniDFA, KearnsVaziraniMealy, LStarBaseAdapterDFA, LStarBaseAdapterMealy, MalerPnueliDFA, MalerPnueliMealy, MalerPnueliMoore, MealyDHC, MealyRefinementCounterLearner, MooreRefinementCounterLearner, NFALearnerWrapper, NLStarLearner, ObservationPackAdapterDFA, ObservationPackAdapterMealy, OPLearnerDFA, OPLearnerMealy, OPLearnerMoore, OPLearnerVPA, OptimalLStarDFA, OptimalLStarMealy, OptimalTTTAdapterDFA, OptimalTTTAdapterMealy, OptimalTTTDFA, OptimalTTTMealy, PrefixTTTLearnerDFA, RefinementCounterLearner, RivestSchapireAdapterDFA, RivestSchapireAdapterMealy, RivestSchapireDFA, RivestSchapireMealy, RivestSchapireMoore, SBALearner, SPALearner, SPMMLearner, TTTAdapterDFA, TTTAdapterMealy, TTTLearnerDFA, TTTLearnerMealy, TTTLearnerMoore, TTTLearnerVPA

public interface LearningAlgorithm<M,I,D>

Basic interface for a model inference algorithm.

Actively inferring models (such as DFAs or Mealy machines) consists of the construction of an initial hypothesis, which is subsequently refined using counterexamples (see EquivalenceOracle).

Nested Class Summary

Nested Classes

Modifier and Type	Interface	Description
static interface	LearningAlgorithm.DFALearner<I>
static interface	LearningAlgorithm.MealyLearner<I,O>
static interface	LearningAlgorithm.MooreLearner<I,O>
static interface	LearningAlgorithm.NFALearner<I>

Method Summary

Modifier and Type	Method	Description
M	getHypothesisModel()	Returns the current hypothesis model.
boolean	refineHypothesis(DefaultQuery<I,D> ceQuery)	Triggers a refinement of the model by providing a counterexample.
void	startLearning()	Starts the model inference process, creating an initial hypothesis in the provided model object.

Method Detail

startLearning

void startLearning()

Starts the model inference process, creating an initial hypothesis in the provided model object. Please note that it should be illegal to invoke this method twice.

refineHypothesis

boolean refineHypothesis(DefaultQuery<I,D> ceQuery)

Triggers a refinement of the model by providing a counterexample. A counterexample is a query which exposes different behavior of the real SUL compared to the hypothesis. Please note that invoking this method before an initial invocation of startLearning() should be illegal.

Parameters:

ceQuery - the query which exposes diverging behavior, as posed to the real SUL (i.e. with the SULs output).

Returns:

true if the counterexample triggered a refinement of the hypothesis, false otherwise (i.e., it was no counterexample).

getHypothesisModel

M getHypothesisModel()

Returns the current hypothesis model.

N.B.: By the contract of this interface, the model returned by this method may not be modified (i.e., M generally should refer to an immutable interface), and its validity is retained only until the next invocation of refineHypothesis(DefaultQuery). If older hypotheses have to be maintained, a copy of the returned model must be made.

Please note that it should be illegal to invoke this method before an initial invocation of startLearning().

Returns:

the current hypothesis model.