Tree transducers, L systems, and two-way machines

A relationship between parallel rewriting systems and two-way machines is investigated. Restrictions on the “copying power” of these devices endow them with rich structuring and give insight into the issues of determinism, parallelism, and copying. Among the parallel rewriting systems considered are the top-down tree transducer; the generalized syntax-directed translation scheme and the ETOL system, and among the two-way machines are the tree-walking automaton, the two-way finite-state transducer, and (generalizations of) the one-way checking stack automaton. The. relationship of these devices to macro grammars is also considered. An effort is made .to provide a systematic survey of a number of existing results

Learning semilinear sets from examples and via queries

AbstractSemilinear sets play an important role in parallel computation models such as matrix grammars, commutative grammars, and Petri nets. In this paper, we consider the problems of learning semilinear sets from examples and via queries. We shall show that (1) the family of semilinear sets is not learnable only from positive examples, while the family of linear sets is learnable only from positive examples, although the problem of learning linear sets from positive examples seems to be computationally intractable; (2) if for any unknown semilinear set Su and any conjectured semilinear set S′, queries whether or not Su⊆S′ and queries whether or not S′⊆Su can be made, there exists a learning procedure which identifies any semilinear set and halts, although the procedure is time-consuming; (3) however, under the same condition, for each fixed dimension, there exist meaningful subfamilies of semilinear sets learnable in polynomial time of the minimum size of representations and, in particular, for any variable dimension, if for any unknown linear set Lu and any conjectured semilinear set S′, queries whether or not Lu⊆S′ can be made, the family of linear sets is learnable in polynomial time of the minimum size of representations and the dimension