hi!
With the aid of SPOT I am developing a counter-example guided synthesizer
in python. See [1] if interested for details.
My question is:
I am trying to parse twa_run in python,
namely, parse the labels of the each step (`spot.impl.step`).
Is there a python way to walk through BDD?
(The label is a simple cube expressed in BDD, and I need to convert it into
my internal structure)
(yes, this is rather a question about python interface for buddy)
(no, that is not feature request:) if not possible, I will simply print the
label and parse the string, that is not crucial)
[1]
The idea is
- guess the model,
- then model check it and get a counter-example
- then encode the counter-example into the "guesser" (which uses an SMT
solver)
thanks,
Ayrat
Hi,
I think there is a bug in the manual of ltlcross. There is the paragraph
> If the translator produced a Streett or Rabin automaton, these columns
> contains the size of a TGBA (or BA) produced by ltlcross from that
> Streett or Rabin automaton. Check in_states, in_edges, in_transitions,
and in_acc for statistics about the actual input automaton.
which seems no longer to be valid as I have no such columns in the output
files created by ltlcross v. 2.4.4.
Best,
Fanda Blahoudek
