Stochastic L-system Inference from Multiple String Sequence Inputs
  Lindenmayer systems (L-systems) are a grammar system that consist of string
rewriting rules. The rules replace every symbol in a string in parallel with a
successor to produce the next string, and this procedure iterates. In a
stochastic context-free L-system (S0L-system), every symbol may have one or
more rewriting rule, each with an associated probability of selection. Properly
constructed rewriting rules have been found to be useful for modeling and
simulating some natural and human engineered processes where each derived
string describes a step in the simulation. Typically, processes are modeled by
experts who meticulously construct the rules based on measurements or domain
knowledge of the process. This paper presents an automated approach to finding
stochastic L-systems, given a set of string sequences as input. The implemented
tool is called the Plant Model Inference Tool for S0L-systems (PMIT-S0L).
PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test
suite, which are each used to generate input strings, and PMIT-S0L is then used
to infer the system from only the sequences. The evaluation shows that PMIT-S0L
infers S0L-systems with up to 9 rewriting rules each in under 12 hours.
Additionally, it is found that 3 sequences of strings is sufficient to find the
correct original rewriting rules in 100% of the cases in the test suite, and 6
sequences of strings reduces the difference in the associated probabilities to
approximately 1% or less.
