Guest editorial to the special issue on grammatical inference

Abstract

Grammarsareapowerfulrepresentationofsequentialpatternsofvarioustypes,rangingfromspeech and other audio signals, to biological sequences and user navigation on the Web. Thelong study of grammars, especially in natural languages, has resulted in a rich repertoire ofgrammar variants and flavors that provide different representation power and consequentlydifferent complexity of analysis. Beyond the issues of representation complexity, most ofthe work on grammars has focused on their use by parsing programs that analyze stringsof a given “language”, in order to prove their grammaticalness and/or identify interestingelements of the language.The use of grammars by parsers, especially the more expressive and complex grammars,remains a challenging and interesting research issue on its own. However, grammatical in-ference goes a step further to study methods that learn grammars from data. Grammaticalinference is an established research field in Artificial Intelligence, dating back to the 60s andhasbeenextensivelyaddressedbyresearchersinautomatatheory,languageacquisition,com-putational linguistics, machine learning, pattern recognition, computational learning theoryand neural networks. From a theoretical perspective, the main aim of this work is to studythe learnability of different types of grammars from different types of data and propose effi-cient algorithms for learning. In parallel, a significant amount of work focuses on innovativeapplications of grammatical inference algorithms to various knowledge discovery tasks.The main forum for presenting this type of work in the past 15 years has been the Interna-tional Colloquium on Grammatical Inference (ICGI) which takes place in different countriesand different continents every two years. The seventh ICGI was held in the National Centrefor Scientific Research “Demokritos”, Greece on October 11–13th, 2004. The topics of the