Generation of Random Automata

When testing our algorithms or equivalence oracles, we would like to have a wide selection of systems under learning, or in the simplest case, automata. It is important to note, that generated automata are not necessarily minimal.

Therefore, with AALpy, you can generate a random automaton for any supported formalism. You simply define the number of states, size of the input (and where applicable output) alphabet. For some formalisms, there are some other miscellaneous options available.

A short case study was conducted on many random automata and results can be seen in the Evaluation section.

So let us explain all possible functions and their parameters. They are imported from aalpy.utils import <function_name>

generate_random_dfa

num_states: number of states
alphabet: input alphabet
num_accepting_states: number of accepting states (Default value = 1)
compute_prefixes: if True, shortest path to reach each state will be computed (Default value = False)
ensure_minimality: if True, random automaton will be minimal, otherwise it could be non-minimal

generate_random_moore_machine and generate_random_mealy_machine

num_states- number of states in the randomly generated automaton
input_alphabet: input alphabet
output_alphabet: output alphabet
compute_prefixes: if True, shortest path to reach each state will be computed (Default value = False)
ensure_minimality: if True, random automaton will be minimal, otherwise it could be non-minimal

generate_random_ONFSM

num_states: number of states
num_inputs: number of inputs
num_outputs: number of outputs
multiple_out_prob: probability that state will have multiple outputs (Default value = 0.1)

generate_random_mdp