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30
Logicbased Genetic Programming with Definite Clause Translation Grammars
 NEW GENERATION COMPUTING
, 2001
"... DCTGGP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context–free languages, and it allows semantic information associated with a language to be easily accomodated by the grammar. T ..."
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Cited by 20 (10 self)
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DCTGGP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context–free languages, and it allows semantic information associated with a language to be easily accomodated by the grammar. This is useful in genetic programming for defining the interpreter of a target language, or incorporating both syntactic and semantic problem–specific contraints into the evolutionary search. The DCTGGP system improves on other grammar–based GP systems by permitting non–trivial semantic aspects of the language to be defined with the grammar. It also automatically analyzes grammar rules in order to determine their minimal depth and termination characteristics, which are required when generating random program trees of varied shapes and sizes. An application using DCTGGP is described.
Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results
, 2003
"... This paper provides a comprehensive survey of the field of grammar induction applied to randomly generated languages using sparse example sets. ..."
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Cited by 19 (0 self)
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This paper provides a comprehensive survey of the field of grammar induction applied to randomly generated languages using sparse example sets.
Evolving Stochastic ContextFree Grammars from Examples Using a Minimum Description Length Principle
 Paper presented at the Workshop on Automata Induction Grammatical Inference and Language Acquisition, ICML97
"... This paper describes an evolutionary approach to the problem of inferring stochastic contextfree 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 evolv ..."
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This paper describes an evolutionary approach to the problem of inferring stochastic contextfree 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 devi...
Learning deterministic finite automata with a smart state labelling evolutionary algorithm
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we des ..."
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Cited by 13 (1 self)
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Abstract—Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple deterministic procedure to optimally assign state labels. We compare its performance with the Evidence Driven State Merging (EDSM) algorithm, one of the most powerful known DFA learning algorithms. We present results on random DFA induction problems of varying target size and training set density. We also study the effects of noisy training data on the evolutionary approach and on EDSM. On noisefree data, we find that our evolutionary method outperforms EDSM on small sparse data sets. In the case of noisy training data, we find that our evolutionary method consistently outperforms EDSM, as well as other significant methods submitted to two recent competitions. Index Terms—Grammatical inference, finite state automata, random hill climber, evolutionary algorithm. 1
Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions
 International Journal of Applied Intelligence
, 1999
"... The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for effi ..."
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Cited by 9 (5 self)
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The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognize a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cutoff value will preempt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognizing stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.
Regular Inference as a graph coloring problem
 In Workshop on Grammar Inference, Automata Induction, and Language Acquisition (ICML' 97
, 1997
"... We consider the problem of learning the set of all most general DFA consistent with a labeled sample. The paper proposess a constraints based specification of the set of solutions and gives an efficient algorithm to build the system of constraints. The effective production of DFA may then be reduced ..."
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Cited by 7 (1 self)
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We consider the problem of learning the set of all most general DFA consistent with a labeled sample. The paper proposess a constraints based specification of the set of solutions and gives an efficient algorithm to build the system of constraints. The effective production of DFA may then be reduced to a graph coloring problem. We have implemented and tested our approach on a classical benchmark. First results are very encouraging and show that the production of all solution DFAs may be practically tractable. Keywords Regular inference, deterministic finite state automata, version space, graph coloring. Introduction The paper addresses the issue of inferring all the solutions of a regular inference problem. More precisely, the problem may be stated as follows: Given a set of positive sentences I + a set of negative sentences I \Gamma Find the set of all automata A verifying: 1. A is a deterministic finite state automata ; 2. I + is structurally complete for A ; 3. L(A), the langua...
How considering incompatible state mergings may reduce the DFA induction search tree
 Fourth International Colloquium on Grammatical Inference (ICGI'98
, 1998
"... . A simple and effective method for DFA induction from positive and negative samples is the state merging method. The corresponding search space may be treestructured, considering two subspaces for a given pair of states: the subspace where states are merged and the subspace where states remain ..."
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Cited by 6 (1 self)
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. A simple and effective method for DFA induction from positive and negative samples is the state merging method. The corresponding search space may be treestructured, considering two subspaces for a given pair of states: the subspace where states are merged and the subspace where states remain different. Choosing different pairs leads to different sizes of space, due to state mergings dependencies. Thus, ordering the successive choices of these pairs is an important issue. Starting from a constraint characterization of incompatible state mergings, we show that this characterization allows to achieve better choices, i.e. to reduce the size of the search tree. Within this framework, we address the issue of learning the set of all minimal compatible DFA's. We propose a pruning criterion and experiment with several ordering criteria. The prefix order and a new entropy based criterion exhibit the best results in our test sets. keywords: grammatical inference, DFA, constraint...
Augmented regular expressions: a formalism to describe, recognize, and learn a class of contextsensitive languages
 Research Report LSI9517R, Universitat Politecnica de Catalunya
, 1995
"... In order to extend the potential of application of the syntactic approach to pattern recognition, the efficient use of models capable of describing contextsensitive structural relationships is needed. Moreover, the ability to learn such models from examples is interesting to automate as much as pos ..."
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Cited by 5 (5 self)
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In order to extend the potential of application of the syntactic approach to pattern recognition, the efficient use of models capable of describing contextsensitive structural relationships is needed. Moreover, the ability to learn such models from examples is interesting to automate as much as possible the development of applications. In this paper, a new formalism that permits to describe a nontrivial class of contextsensitive languages, the Augmented Regular Expressions (AREs), is introduced. AREs augment the descriptive power of regular expressions by including a set of constraints that involve the number of instances of the operands of the star operations in each string of the language. Likewise, algorithms are given to infer AREs from string examples and to recognize language strings by AREs. The method for learning AREs consists of a regular grammatical inference step, aimed at obtaining a regular superset of the target language, followed by a constraint induction process, which reduces the extension of the inferred language transforming it into a contextsensitive one. Hence, this twostep approach avoids the difficulty of learning contextsensitive grammars directly from the data. The method for recognizing language strings is also splitted in two stages: matching the underlying regular expression and checking that the resulting star instances satisfy the constraints. 1
Incrementally Inferring ContextFree Grammars for DomainSpecific Languages
 In Proceedings of the Eighteenth International Conference on Software Engineering and Knowledge Engineering (SEKE'06
, 2006
"... Grammatical inference (or grammar inference) has been applied to various problems in areas such as computational biology, and speech and pattern recognition but its application to the programming language problem domain has been limited. We propose a new application area for grammar inference which ..."
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Cited by 5 (3 self)
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Grammatical inference (or grammar inference) has been applied to various problems in areas such as computational biology, and speech and pattern recognition but its application to the programming language problem domain has been limited. We propose a new application area for grammar inference which intends to make domainspecific language development easier and finds a second application in renovation tools for legacy software systems. We discuss the improvements made to our core incremental approach to inferring contextfree grammars. The approach affords a number of advancements over our previous geneticprogramming based inference system. We discuss the beam search heuristic for improved searching in the solution space of all grammars, the Minimum Description Length heuristic to direct the search towards simpler grammars, and the righthandside subset constructor operator. 1.
Literature Survey
, 1999
"... Both genetic algorithms and neural networks are machine learning techniques inspired by nature. By mimicking, although in a simplied way, biological processes, new alternatives in problem solving can be explored. We are interested in the application of these strategies in the eld of natural langu ..."
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Cited by 4 (0 self)
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Both genetic algorithms and neural networks are machine learning techniques inspired by nature. By mimicking, although in a simplied way, biological processes, new alternatives in problem solving can be explored. We are interested in the application of these strategies in the eld of natural language processing. Here we provide an overview of previously published material in this area. We trace which domains of natural language processing have been investigated with evolutionary or neural strategies, discuss approaches and experiments, and provide an introduction to the literature. 1 Introduction Both evolutionary algorithms and articial neural networks mimick aspects of biological processes. In this section we introduce these methods and point to the general literature. 1.1 Evolutionary Computing The idea of using evolutionary computation as a problem solving technique exists since the 1950s (Box, 1957), (Bledsoe, 1961), (Fraser, 1957). Since then, four major approaches ha...