Connectionist learning of regular graph grammars

This paper presents a new connectionist approach to grammatical inference. Using only positive examples, the algorithm learns regular graph grammars, representing two-dimensional iterative structures drawn on a discrete Cartesian grid. This work is intended as a case study in connectionist symbol processing andgeometric concept formation. A grammar is represented by a self-configuring connectionist network that is analogous to a transition diagram except that it can deal with graph grammars as easily as string grammars. Learning starts with a trivial grammar, expressing nogrammatical knowledge, which is then refined, by a process of successive node splitting and merging, into a grammar adequate to describe the population of input patterns. In conclusion, I argue that the connectionist style of computation is, in some ways, better suited than sequential computation to the task of representing and manipulating recursive structures

Symbolic and connectionist learning techniques for grammatical inference

This thesis is structured in four parts for a total of ten chapters. The first part, introduction and review (Chapters 1 to 4), presents an extensive state-of-the-art review of both symbolic and connectionist GI methods, that serves also to state most of the basic material needed to describe later the contributions of the thesis. These contributions constitute the contents of the rest of parts (Chapters 5 to 10). The second part, contributions on symbolic and connectionist techniques for regular grammatical inference (Chapters 5 to 7), describes the contributions related to the theory and methods for regular GI, which include other lateral subjects such as the representation oí. finite-state machines (FSMs) in recurrent neural networks (RNNs).The third part of the thesis, augmented regular expressions and their inductive inference, comprises Chapters 8 and 9. The augmented regular expressions (or AREs) are defined and proposed as a new representation for a subclass of CSLs that does not contain all the context-free languages but a large class of languages capable of describing patterns with symmetries and other (context-sensitive) structures of interest in pattern recognition problems.The fourth part of the thesis just includes Chapter 10: conclusions and future research. Chapter 10 summarizes the main results obtained and points out the lines of further research that should be followed both to deepen in some of the theoretical aspects raised and to facilitate the application of the developed GI tools to real-world problems in the area of computer vision

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía