Paper Info
Title
Inferring Temporal Parametric L-systems Using Cartesian Genetic Programming
Abstract
Lindenmayer Systems (L-systems) are formal grammars that use rewriting rules to replace, in parallel, every symbol in a string with a replacement string. By iterating, a sequence of strings is produced whose symbols can model temporal processes by interpreting them as simulation instructions. Among the types of L-systems, parametric L-systems are considered useful for simulating mechanisms that change based on different influences as the parameters change. Typically, L-systems are found by taking precise measurements and using existing knowledge, which can be addressed by automatic inference. This paper presents the Plant Model Inference Tool for Parametric L-systems (PMIT-PARAM) that can automatically learn parametric L-systems from a sequence of strings generated, where at least one parameter represents time. PMIT-PARAM is evaluated on a test suite of 20 known parametric L-systems, and is found to be able to infer the correct rewriting rules for the 18 L-systems containing only non-erasing productions; however, it can find appropriate parametric equations for all 20 of the L-systems. Inferring L-systems algorithmically not only can automatically learn models and simulations of a process with potentially less effort than doing so by hand, but it may also help reveal the scientific principles governing how the process' mechanisms change over time.
Year
DOI
Venue
2020
10.1109/ICTAI50040.2020.00095
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
DocType
ISSN
Lindenmayer Systems,Parametric L-systems,Inductive Inference,Plant Modeling,Natural Process Modeling
Conference
1082-3409
ISBN
Citations 
PageRank 
978-1-7281-8536-1
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Jason Bernard100.34
Ian McQuillan29724.72