A Bibliography on Fuzzy Automata, Grammars and Lanuages

This bibliography contains references to papers on fuzzy formal languages, the generation of fuzzy languages by means of fuzzy grammars, the recognition of fuzzy languages by fuzzy automata and machines, as well as some applications of fuzzy set theory to syntactic pattern recognition, linguistics and natural language processing

A Fuzzy Petri Nets Model for Computing With Words

Motivated by Zadeh's paradigm of computing with words rather than numbers, several formal models of computing with words have recently been proposed. These models are based on automata and thus are not well-suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent computing, Petri nets, together with fuzzy set theory and thereby establish a concurrency model of computing with words--fuzzy Petri nets for computing with words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of transitions are some special words modeled by fuzzy sets. By employing the methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which makes it possible for computing with more words. The language expressiveness of the two formal models of computing with words, fuzzy automata for computing with words and FPNCWs, is compared as well. A few small examples are provided to illustrate the theoretical development.Comment: double columns 14 pages, 8 figure

Developmental constraints on learning artificial grammars with fixed, flexible and free word order

Human learning, although highly flexible and efficient, is constrained in ways that facilitate or impede the acquisition of certain systems of information. Some such constraints, active during infancy and childhood, have been proposed to account for the apparent ease with which typically developing children acquire language. In a series of experiments, we investigated the role of developmental constraints on learning artificial grammars with a distinction between shorter and relatively frequent words (‘function words,’ F-words) and longer and less frequent words (‘content words,’ C-words). We constructed 4 finite-state grammars, in which the order of F-words, relative to C-words, was either fixed (F-words always occupied the same positions in a string), flexible (every F-word always followed a C-word), or free. We exposed adults (N = 84) and kindergarten children (N = 100) to strings from each of these artificial grammars, and we assessed their ability to recognize strings with the same structure, but a different vocabulary. Adults were better at recognizing strings when regularities were available (i.e., fixed and flexible order grammars), while children were better at recognizing strings from the grammars consistent with the attested distribution of function and content words in natural languages (i.e., flexible and free order grammars). These results provide evidence for a link between developmental constraints on learning and linguistic typology