aalpy 1.4.2

An active automata learning library

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AALpy is a light-weight automata learning library written in Python. You can start learning models of black-box systems with a few lines of code.

AALpy supports both active and passive automata learning algorithms that can be used to learn a variety of modeling formalisms, including deterministic, non-deterministic, and stochastic automata, as well as deterministic context-free grammars/pushdown automata.

Automata Type	Supported Formalisms	Algorithms	Features
Deterministic	DFAs Mealy Machines Moore Machines	L* KV RPNI	Seamless Caching Counterexample Processing 13 Equivalence Oracles
Non-Deterministic	ONFSM Abstracted ONFSM	L*ONFSM	Size Reduction Trough Abstraction
Stochastic	Markov Decision Processes Stochastic Mealy Machines Markov Chains	L*MDP L*SMM ALERGIA	Counterexample Processing Exportable to PRISM format Bindings to jALERGIA
Pushdown	VPA/SEVPA	KVVPA PAPNI	Passive learning of VPAs Exclusive call-return pairs

Installation

Use the package manager pip to install the latest release of AALpy:

pip install aalpy

To install current version of the master branch (it might contain bugfixes and added functionalities between releases):

pip install https://github.com/DES-Lab/AALpy/archive/master.zip

The minimum required version of Python is 3.6.
Ensure that you have Graphviz installed and added to your path if you want to visualize models.

For manual installation, clone the repo and install pydot (the only dependency).

Documentation and Wiki

If you are interested in automata learning or would like to understand the automata learning process in more detail, please check out our Wiki. On Wiki, you will find more detailed examples on how to use AALpy.

• https://github.com/DES-Lab/AALpy/wiki

Examples.py contains examples covering almost the whole of AALpy's functionality and its a great starting point.

Usage

All active automata learning procedures follow this high-level approach:

• Define the input alphabet and system under learning (SUL)
• Choose the equivalence oracle
• Run the learning algorithm

Passive learning algorithm simply require you to provide data in the appropriate format (check Wiki and Examples) and run the learning function.

Code snipped demonstrating some of AALpy's functionalities

The following snippet demonstrates a short example in which an automaton is either loaded or randomly generated and then learned.

from aalpy.utils import load_automaton_from_file, generate_random_deterministic_automata
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_Lstar, run_KV

# load an automaton
# automaton = load_automaton_from_file('path_to_the_file.dot', automaton_type='dfa')

# or randomly generate one
random_dfa = generate_random_deterministic_automata(automaton_type='dfa', num_states=8, 
                                                    input_alphabet_size=5, output_alphabet_size=2)

# get input alphabet of the automaton
alphabet = random_dfa.get_input_alphabet()

# loaded or randomly generated automata are considered as BLACK-BOX that is queried
# learning algorithm has no knowledge about its structure
# create a SUL instance for the automaton/system under learning
sul = AutomatonSUL(random_dfa)

# define the equivalence oracle
eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09)

# start learning
# run_KV is for the most part reacquires much fewer interactions with the system under learning
learned_dfa = run_KV(alphabet, sul, eq_oracle, automaton_type='dfa')
# or run L*
# learned_dfa_lstar = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa')

# save automaton to file and visualize it
# save_automaton_to_file(learned_dfa, path='Learned_Automaton', file_type='dot')
# or
learned_dfa.save()

# visualize automaton
# visualize_automaton(learned_dfa)
learned_dfa.visualize()
# or just print its DOT representation
print(learned_dfa)

To make experiments reproducible, define a random seed at the beginning of your program.

from random import seed
seed(2) # all experiments will be reproducible

Selected Applications

AALpy has been used to:

• Learn Models of Bluetooth Low-Energy
• Find bugs in VIM text editor
• Learn Input-Output Behavior of RNNs
• Learn Models of GIT
• Solve RL Problems

Cite AALpy and Research Contact

If you use AALpy in your research, please cite us with of the following:

• Extended version (preferred)
• Tool paper

If you have research suggestions or you need specific help concerning your research, feel free to start a discussion or contact [e-mail]. We are happy to help you and consult you in applying automata learning in various domains.

Contributing

Pull requests are welcome. For significant changes, please open an issue first to discuss what you would like to change. In case of any questions or possible bugs, please open issues.