Name
Abstract | ||
|---|---|---|
Stochastic context-free Lindenmayer systems (S0L-systems) are a formal grammar system that produce sequences of strings based on parallel rewriting rules over a probability distribution. The resulting words can be treated as symbolic instructions to create visual models by simulation software. S0L-system have been used to model different natural and engineered processes. One issue with S0L-systems is the difficulty in determining an S0L-systems to model a process. Current approaches either infer S0L-systems based on aesthetics or rely on a priori expert knowledge. This work introduces PMIT-S0L, a tool for inferring S0L-systems from a sequence of strings generated by a (hidden) L-system, using a greedy algorithm hybridized with search algorithms. PMIT-S0L was evaluated using 3600 procedurally generated S0L-systems and is able to infer the test set with 100% success so long as there are 12 or less rewriting rules in total in the L-system. This makes PMIT-S0L applicable for many practical applications. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICTAI.2018.00097 | 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
Lindenmayer Systems, Stochastic L-systems, Inductive Inference, Plant Modeling, Natural Process Modeling | Search algorithm,Simulation software,Computer science,Greedy algorithm,Theoretical computer science,Probability distribution,Software,Artificial intelligence,Rewriting,Formal grammar,Machine learning,Test set | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-5386-7450-5 | 1 |
PageRank | References | Authors |
0.39 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason Bernard | 1 | 2 | 1.45 |
| Ian McQuillan | 2 | 97 | 24.72 |