Editing L-system (section)

=== L-system Inference ===

The idea of automating L-system inference emerged to address the inefficiencies of manual methods, which often required extensive expertise, measurements, and trial-and-error processes. This automation aimed to enable the inference of L-systems directly from observational data, eliminating the need for manual encoding of rules.

Initial algorithms primarily targeted deterministic context-free L-systems (D0L-systems), which are among the simplest types of L-systems. These early efforts demonstrated the feasibility of automatic inference but were severely limited in scope, typically handling only systems with small alphabets and simple rewriting rules.<ref>Bian Runqiang, Phoebe Chen, Kevin Burrage, Jim Hanan, Peter Room, and John Belward. Derivation of L-system models from measurements of biological branching structures using genetic algorithms. In Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 514524. Springer, 2002.</ref><ref name=":2">Ryohei Nakano. Emergent induction of deterministic context-free L-system grammar. In Innovations in Bio-inspired Computing and Applications, pages 7584. Springer International Publishing, 2014.</ref><ref>P. G. Doucet. The syntactic inference problem for D0L-sequences. L Systems, pages 146161, 1974</ref><ref name=":3">Roger Curry. On the evolution of parametric L-systems. Technical report, University of Calgary, 2000.</ref> For instance, Nakano's <ref name=":2" /> work highlighted the challenges of inferring L-systems with larger alphabets and more complex structures, describing the task as "immensely complicated".