Comments (2)
You are correct, currently the parameter is independent of the hypothesis size and used to insert all tuples of length l \in {0, ..., maxDepth}
between elements of the P and W sets. I'll have to look at the paper to see what the authors intentions for this case are (I haven't originally implemented it in AutomataLib and only know the Wp-method with maxDepth = 0
).
What I see as a potential problem with shrinking the value is, that a bad estimate will degrade performance in later equivalence checks, because once n > m you basically have no more look-ahead. With a fixed exploration depth (as it is now), you continue to look ahead even in later iterations of refinement cycles. On the contrary, a too low value for the current value of maxDepth
probably gives you the same problems in the earlier equivalence checks. Maybe, we can use two parameters (one for m
and one for the minimal exploration depth depth
), and always choose max(depth, m - n)
, so users have more fine-grained control over the behaviour. What do you think?
For now, to simulate shrinking exploration depth, you can just instantiate a new equivalence for every refinement loop, like (untested):
DFALearner<I> learner = ...;
learner.startLearning();
int m = ...;
boolean finished = false;
while (!finished) {
DFA<?, I> hyp = learner.getHypothesisModel();
DFAEquivalenceOracle<I> eqOracle = new DFAWpMethodEQOracle<>(mqOracle, m - hyp.size());
DefaultQuery<I, Boolean> ce;
if ((ce = eqOracle.findCounterExample(hyp, inputs)) != null) {
learner.refineHypothesis(ce);
} else {
finished = true;
}
}
from automatalib.
Thanks for the quick answer.
My main point is that if I know the upper bound size for the automaton I want to learn (m
in the paper), having a fixed value will require a bunch of unnecessary queries when n
(size of current hypothesis) gets bigger. But I agree that a bad estimate (or no information) can also be a problem.
Your solution makes sense to me: m
would be the upper bound (which can be optional), and depth
the minimal exploration depth (in the worst case, i.e. if no counterexamples are found before).
from automatalib.
Related Issues (20)
- CompactMealyTransition does not extend MealyTransition HOT 5
- Error testing serilaization-dot HOT 10
- Replace JSR305 annotations
- Write a parser for LTSmin formulae
- Incorrect characterizing set for Mealy machine HOT 3
- TAF writer does not enquote outputs of mealy machines
- DOTParsers.mealy() not parsing all transitions HOT 13
- Minimizer.minimize overload without start states is broken HOT 2
- CompactMoore HOT 9
- Deviation in the BBC functionality between (at least) LearnLib 0.14.0 and 0.16.0 HOT 2
- PaigeTarjanMinimization.minimizeDFA does not guarantee minimal result HOT 2
- Add support for Java 17 (LTS) to CI pipeline
- AUTParser only adds states with outgoing transitions
- How to minimize a chained mealy automata HOT 10
- M3C does not run on M1 MacBooks HOT 1
- Fail on javadoc warnings and improve documentation
- M3CParser fails when ID is a formula token
- IndexOutOfBoundsException in IncrementalMealyDAGBuilder when input word is longer than output word HOT 6
- IncrementalDAGBuilder fails for traces with cyclic repetitions longer than 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from automatalib.