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Lindenmayer systems (L-systems) are a grammar system that consist of stringrewriting rules. The rules replace every symbol in a string in parallel with asuccessor to produce the next string, and this procedure iterates. In astochastic context-free L-system (S0L-system), every symbol may have one ormore rewriting rule, each with an associated probability of selection. Properlyconstructed rewriting rules have been found to be useful for modeling andsimulating some natural and human engineered processes where each derivedstring describes a step in the simulation. Typically, processes are modeled byexperts who meticulously construct the rules based on measurements or domainknowledge of the process. This paper presents an automated approach to findingstochastic L-systems, given a set of string sequences as input. The implementedtool is called the Plant Model Inference Tool for S0L-systems (PMIT-S0L).PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a testsuite, which are each used to generate input strings, and PMIT-S0L is then usedto infer the system from only the sequences. The evaluation shows that PMIT-S0Linfers 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 thecorrect original rewriting rules in 100% of the cases in the test suite, and 6sequences of strings reduces the difference in the associated probabilities toapproximately 1% or less.

Stochastic L-system Inference from Multiple String Sequence Inputs