207106066
Families of Noncounting Languages and Their Learnability from Positive Data
This paper introduces some subclasses of noncounting languages and presents some results on the learnability of the classes from positive data. We first establish several relationships among the language classes introduced and the class of reversible languages. Especially, we introduce the notion of local parsability, and define a class (k, l)-CLTS, which is a subclass of the class of concatenations of strictly locally testable languages. We show its close relation to the class of reversible languages. We then study on the relationship between the closure of the Boolean operations and the learnability in the limit from positive data only. Further, we explore the learnability question of some subclasses of noncounting languages in the model of identification in the limit from positive data. In particular, we show that, for each k, l≥1, (k, l)-CLTS is identifiable in the limit from positive data using reversible automata with the conjectures updated in polynomial time. Some possible applications of the result are also briefly discussed.
28066504
Multiple splicing systems and the universal computability
We propose a new extension of splicing systems, called multiple splicing systems, based on a kind of logic grammars. First, we introduce a class of Elementary formal systems, called simple H-form EFS, and show that its generative power is equivalent to the class of basic splicing systems (the original Head's splicing system) and is more adequate as representation device for formal languages. Next, we gradually extend the simple class of EFSs and get a very natural extension of splicing systems, multiple splicing systems. We show that multiple splicing systems have universal computability.
32287187
Sticker systems with complex structures
Abstract In this paper, we propose a variant of sticker systems which uses molecules with complex structures. Since the original sticker systems (Paun et al. (1998) [2, 8]) working on double strands of DNA have been studied as a formal model for self-assembly in DNA computing, we extend the sticker systems to working on more complex (higher-order) structures of DNA molecules. The advantage of sticker systems with complex structures is that augmented with weak codings we can obtain the characterization of recursively enumerable languages by using only sticking (hybridization) operations for complex molecules, while the usual sticker systems require more complicated operations such as the simultaneous use of couples of dominoes or coherent computations besides morphisms.
40461039
Learning local languages and its application to protein /spl alpha/-chain identification
Concerns an efficient algorithm for learning in the limit a special type of regular language called a locally testable language from positive data, and its application to identifying the protein /spl alpha/-chain region in amino acid sequences. First, we present a linear-time algorithm that, given a locally testable language, learns (identifies) its deterministic finite state automaton in the limit from only positive data. This provides a practical and efficient learning method for a specific domain of symbolic analysis. We then describe several experimental results using the learning algorithm. Following a theoretical observation which strongly suggests that a certain type of amino acid sequence can be expressed by a locally testable language, we apply the learning algorithm to identifying the protein /spl alpha/-chain region in amino acid sequences for hemoglobin. Experimental scores show an overall success rate of 95% correct identification for positive data and 96% for negative data.<<ETX>>
26602421
Learning Approximately Regular Languages with Reversible Languages
In this note, we consider the problem of learning approximately regular languages in the limit from positive data using the class of k-reversible languages. The class of k-reversible languages was introduced by Angluin (1982), and proved to be efficiently identifiable in the limit from positive data only. We show that Angluin's learning algorithm for the class of k-reversible languages can be readily adopted for the approximate identification of regular languages from positive data. Considering the negative result on the exact identifiability by Gold (1967), this approximation approach would be one of the best we could hope for learning the class of regular languages from positive data only.
62399989
Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
28922785
Realizability Interpretation of Generalized Inductive Definitions
Generalized inductive definitions give a way of defining a predicate as the least solution P of the equation [email protected]?A[P] where a predicate variable P may occur in a formula A[P] positively. This paper gives aq-realizability interpretation of generalized inductive definitions and proves the soundness of the interpretation.