Even linear simple matrix languages: formal language properties and grammatical inference

We show that so-called deterministic even linear simple matrix grammars can be inferred in polynomial time using the query-based learner-teacher model (minimally adequate teacher-learning model) proposed by Angluin (Inform. and Comput. 75 (1987) 87) for learning deterministic regular languages. In this way, we extend the class of efficiently learnable languages beyond both the even linear languages and the even equal matrix languages (Pattern Recognition 21 (1988) 55; Proc. 2nd Internat. Colloq. on Grammatical Inference (ICGI-94): Grammatical Inference and Applications, Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence, vol. 862, Springer, Berlin, 1994, p. 38; Inform. Process. Lett. 28 (1988) 193; Technical Report IIAS-RR-93-6E, Fujitsu Laboratories, 1992; Parallel Image Analysis, ICPIA'92, Lecture Notes in Computer Science, vol. 652, Springer, Berlin, 1992, p. 274; Inform. and Comput. 123 (1995) 138; Algorithmic Learning for Knowledge-Based Systems, Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence, Springer, Berlin, 1995, p. 317). Moreover, we investigate formal language properties of even linear simple matrix languages and related language classes. More precisely, we discuss characterizations, (proper) inclusion relations, closure properties and decidability questions. This way, we also show that, in a certain sense, the idea of iterating the control language approach for learning purposes, as undertook by Takada (1995), could be seen as a special case of using deterministic even linear simple matrix grammars as basic and uniform learning target.