On the computational completeness of generalized forbidding matrix grammars

Matrix grammars are one of the first approaches ever proposed in regulated rewriting, prescribing that rules have to be applied in a certain order. In traditional regulated rewriting, the most interesting case shows up when all rules are context-free. Typical descriptional complexity measures incorporate the number of nonterminals or the length, i.e., the number of rules per matrix. When viewing matrices as program fragments, it becomes natural to consider additional applicability conditions for such matrices. Here, we focus on forbidding sets, i.e., a matrix is applicable to a sentential form w only if none of the words in its forbidding set occurs as a subword in w. This gives rise to further natural descriptional complexity measures: How long could words in forbidding sets be? How many words could be in any forbidding set? How many matrices contain non-empty forbidding contexts? As context-free grammars with forbidding sets are known as generalized forbidding grammars, we call this variant of matrix grammars also generalized forbidding. In this paper, we attempt to answer the four questions above while studying the computational completeness of generalized forbidding matrix grammars. In the course of our studies, we also define several new normal forms for type-0 grammars that might be of independent interest. •Combining conditional matrix (KM) and generalized forbidding (GF) grammars, we define generalized forbidding matrix grammars.•We tackle the question how small measures of descriptional complexity could be while maintaining computational completeness.•Our studies also add new results to the theory of KM and of GF grammars.•We introduce a new normal form for type-0 grammars that was useful for us and that might be also of independent interest.