Scott C. Livingston <slivingston@caltech.edu>
btsynth is an implementation of the "backtracking synthesis" algorithm, currently only applied to gridworlds. The dependencies include TuLiP and NumPy.
Some functionality remains undocumented, but a good start is
printworld.py for viewing gridworld data files, and
gridworld_example.py for running
examples. dgridworld_example.py is the deterministic case, i.e.,
no adversarial environment. E.g., try:
$ tools/printworld.py examples/data/paper4{,_real}.world
$ examples/gridworld_example.py examples/data/paper4{,_real}.world
To generate a random gridworld problem of size 4 by 10, try:
$ tools/printworld.py -r 4 10
The output is a pretty visualization and a world code that can be
saved to a plaintext file for later use (see below). The problem thus
generated may not be feasible. A simple-minded but correct way to
automatically test feasibility is to attempt global synthesis on it by
calling gen_navobs_soln (see gridworld_example.py for example
usage).
The code includes an extension to the Automaton class defined in TuLiP. Examples of new features are finite memory and switched transitions dependent on the state of this memory.
To test btsynth, from the root directory run:
$ nosetests
More detailed documentation is under doc directory, written in
reStructuredText for Sphinx. A snapshot of this documentation will
occasionally be posted at http://vehicles.caltech.edu/scott/btsynth/