Affiliation:
1. Department of Computer Science, University of Stellenbosch, South Africa
2. Department of Mathematics, University of Stellenbosch, South Africa
Abstract
We use random context picture grammars to generate pictures through successive refinement. The productions of such a grammar are context-free, but their application is regulated — "permitted" or "forbidden" — by context randomly distributed in the developing picture. Grammars using this relatively weak context often succeed where context-free grammars fail, e.g. in generating the Sierpiński carpets. On the other hand it proved possible to develop iteration theorems for three subclasses of these grammars, namely a pumping–shrinking, a pumping and a shrinking lemma for context-free, random permitting and random forbidding context picture grammars, respectively. Finding necessary conditions is problematic in the case of most models of context-free grammars with context-sensing ability, since they consider a variable and its context as a finite connected array. We have already shown that context-free picture grammars are strictly weaker than both random permitting and random forbidding context picture grammars, also that random permitting context is strictly weaker than random context. We now show that grammars which use forbidding context only are strictly weaker than random context picture grammars.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Bag context picture grammars;Journal of Computer Languages;2019-04
2. Generalized Random Context Picture Grammars: The State of the Art;Lecture Notes in Computer Science;2012
3. TABLE-DRIVEN CONTEXT-FREE PICTURE GRAMMARS;International Journal of Foundations of Computer Science;2007-12
4. A property of random context picture grammars;Theoretical Computer Science;2003-05