Random Context and Semi-Conditional Insertion-Deletion Systems

Sergiu Ivanov, Sergey Verlan

In this article we introduce the operations of insertion and deletion working in a random-context and semi-conditional manner. We show that the conditional use of rules strictly increase the computational power. In the case of semi-conditional insertion-deletion systems context-free insertion and deletion rules of one symbol are sufficient to get the computational completeness. In the random context case our results expose an asymmetry between the computational power of insertion and deletion rules: systems of size $(2,0,0; 1,1,0)$ are computationally complete, while systems of size $(1,1,0;2,0,0)$ (and more generally of size $(1,1,0;p,1,1)$) are not. This is particularly interesting because other control mechanisms like graph-control or matrix control used together with insertion-deletion systems do not present such asymmetry.

Knowledge Graph

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