Formal language

About: Formal language is a research topic. Over the lifetime, 5763 publications have been published within this topic receiving 154114 citations.

A theory of discrete patterns and their implementation in SNOBOL4

Abstract: The notion of a discrete pattern is formalized and certain properties deduced. A pattern is shown to be a generalization of a formal language. Algorithms for implementing the kinds of patterns in SNOBOL4 are given. The general approach is to create, in-so-far as possible, a bottom-up parse from a top-down specification.

Optimized temporal monitors for SystemC

Abstract: SystemC is a modeling language built as an extension of C++. Its growing popularity and the increasing complexity of designs have motivated research efforts aimed at the verification of SystemC models using assertion-based verification (ABV), where the designer asserts properties that capture the design intent in a formal language such as PSL or SVA. The model then can be verified against the properties using runtime or formal verification techniques. In this paper we focus on automated generation of runtime monitors from temporal properties. Our focus is on minimizing runtime overhead, rather than monitor size or monitor-generation time. We identify four issues in monitor generation: state minimization, alphabet representation, alphabet minimization, and monitor encoding. We conduct extensive experimentation and identify a combination of settings that offers the best performance in terms of runtime overhead.

Parsing of General Context-Free Languages

Abstract: Publisher Summary One of the major advances both in the study of natural languages and in the use of newly defined languages, such as programming languages, came with the realization that one required a formal and precise mechanism for generating the infinite set of strings of a language. Both programming linguists and natural linguists independently formulated the notion of a context-free grammar as an important generative schema. This chapter focuses on this recognition problem and its related problem of “parsing,” which means to find a derivation tree of a string in the language. A variety of methods are now known for parsing classes of context-free grammars. In some sense, the crudest method is systematic trial-and-error—that is, a deterministic simulation of the nondeterministic choice of next steps in a derivation. However, such a simulation can require a number of steps, which is exponential in the length of the string being analyzed. The chapter focuses its attention on those classes of grammars that are rich enough to generate all the context-free languages. It concentrates on three algorithms for parsing classes of context-free grammars. It shows that each method parses a class of grammars sufficiently large to generate all the context-free languages. Furthermore, each method has a time bound, which is shown to be at worst cubic in the length of the string being parsed. The three methods are presented within a consistent framework and notation so that it is possible to understand both their similarities and their differences.

Evolving stochastic context-free grammars from examples using a minimum description length principle

Abstract: This paper describes an evolutionary approach to the problem of inferring stochastic context-free grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. We provide details of our fitness function for grammars and present the results of a number of experiments in learning grammars for a range of formal languages. Keywords: grammatical inference, genetic algorithms, language modelling, formal languages, induction, minimum description length. Introduction Grammatical inference (Gold 1978) is a fundamental problem in many areas of artificial intelligence and cognitive science, including speech and language processing, syntactic pattern recognition and automated programming. Although a wide variety of techniques for automated grammatical inference have been devis..