Comparación de dos algoritmos recientes para inferencia gramatical de lenguajes regulares mediante autómatas no deterministas

El desarrollo de nuevos algoritmos, que resulten convergentes y eficientes, es un paso necesario para un uso provechoso de la inferencia gramatical en la solución de problemas reales y de mayor tamaño. En este trabajo se presentan dos algoritmos llamados DeLeTe2 y MRIA, que implementan la inferencia gramatical por medio de autómatas no deterministas, en contraste con los algoritmos más comúnmente empleados, los cuales utilizan autómatas deterministas. Se consideran las ventajas y desventajas de este cambio en el modelo de representación, mediante la descripción detallada y la comparación de los dos algoritmos de inferencia con respecto al enfoque utilizado en su implementación, a su complejidad computacional, a sus criterios de terminación y a su desempeño sobre un cuerpo de datos sintéticos

Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares

Esta investigación aborda el tema del diseño de algoritmos de inferencia gramatical para lenguajes regulares, particularmente en lo relacionado con la mezcla de estados como elemento fundamental del proceso de inferencia. Se estudia la mezcla de estados en sus variantes determinista y no determinista desde el punto de vista teórico. Como resultado se propone una manera eficiente de realizar la mezcla de estados no determinista y se demuestra que la inferencia gramatical de lenguajes regulares basada en la mezcla de estados (tanto determinista como no determinista) converge en el límite independientemente del orden en que se realizan las mezclas. La demostración es de interés ya que entre otras consecuencias, permite afirmar la convergencia en el límite de la estrategia EDSM (Evidence Driven States Merging) que es ampliamente conocida en la literatura como un heurísico. Dado que la demostración considera también la inferencia de autómatas no deterministas, el resultado abre la puerta al desarrollo de algoritmos convergentes que infieren autómatas no deterministas. El aspecto experimental de esta investigación propone un conjunto de algoritmos de inferencia gramatical para lenguajes regulares, todos ellos convergentes en el límite. Estos algoritmos surgen de aplicar diferentes variantes de mezcla de estados determinista y no determinista; ellos buscan aprovechar la información que se puede obtener a partir de las relaciones de inclusión entre los lenguajes por la derecha asociados a los estados de todo autómata. Se proponen cuatro algoritmos que hacen mezcla determinista y dos que hacen mezcla no determinista de estados. Los resultados obtenidos al comparar estos nuevos algoritmos con algoritmos de referencia como RPNI, red-blue o DeLeTe2 muestran que se logra disminuir significativamente el tamaño de las hipótesis que se producen, al tiempo que se consiguen tasas de reconocimiento comparables o ligeramente inferiores. También se han obtenido algunas mejoras en la coÁlvarez Vargas, GI. (2007). Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1957Palanci

Genetic Programming Techniques in Engineering Applications

2012/2013Machine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples. Machine learning systems, thus, may automatically synthesize programs from data. This approach is often feasible and cost-effective where manual programming or manual algorithm design is not. In the last decade techniques based on machine learning have spread in a broad range of application domains. In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of engineering applications grounded in real world problems. The problems treated in this work range from the automatic synthesis of regular expressions, to the generation of electricity price forecast, to the synthesis of a model for the tracheal pressure in mechanical ventilation. The results demonstrate that Genetic Programming is indeed a suitable tool for solving complex problems of practical interest. Furthermore, several results constitute a significant improvement over the existing state-of-the-art. The main contribution of this thesis is the design and implementation of a framework for the automatic inference of regular expressions from examples based on Genetic Programming. First, we will show the ability of such a framework to cope with the generation of regular expressions for solving text-extraction tasks from examples. We will experimentally assess our proposal comparing our results with previous proposals on a collection of real-world datasets. The results demonstrate a clear superiority of our approach. We have implemented the approach in a web application that has gained considerable interest and has reached peaks of more 10000 daily accesses. Then, we will apply the framework to a popular "regex golf" challenge, a competition for human players that are required to generate the shortest regular expression solving a given set of problems. Our results rank in the top 10 list of human players worldwide and outperform those generated by the only existing algorithm specialized to this purpose. Hence, we will perform an extensive experimental evaluation in order to compare our proposal to the state-of-the-art proposal in a very close and long-established research field: the generation of a Deterministic Finite Automata (DFA) from a labelled set of examples. Our results demonstrate that the existing state-of-the-art in DFA learning is not suitable for text extraction tasks. We will also show a variant of our framework designed for solving text processing tasks of the search-and-replace form. A common way to automate search-and-replace is to describe the region to be modified and the desired changes through a regular expression and a replacement expression. We will propose a solution to automatically produce both those expressions based only on examples provided by user. We will experimentally assess our proposal on real-word search-and-replace tasks. The results indicate that our proposal is indeed feasible. Finally, we will study the applicability of our framework to the generation of schema based on a sample of the eXtensible Markup Language documents. The eXtensible Markup Language documents are largely used in machine-to-machine interactions and such interactions often require that some constraints are applied to the contents of the documents. These constraints are usually specified in a separate document which is often unavailable or missing. In order to generate a missing schema, we will apply and will evaluate experimentally our framework to solve this problem. In the final part of this thesis we will describe two significant applications from different domains. We will describe a forecasting system for producing estimates of the next day electricity price. The system is based on a combination of a predictor based on Genetic Programming and a classifier based on Neural Networks. Key feature of this system is the ability of handling outliers-i.e., values rarely seen during the learning phase. We will compare our results with a challenging baseline representative of the state-of-the-art. We will show that our proposal exhibits smaller prediction error than the baseline. Finally, we will move to a biomedical problem: estimating tracheal pressure in a patient treated with high-frequency percussive ventilation. High-frequency percussive ventilation is a new and promising non-conventional mechanical ventilatory strategy. In order to avoid barotrauma and volutrauma in patience, the pressure of air insufflated must be monitored carefully. Since measuring the tracheal pressure is difficult, a model for accurately estimating the tracheal pressure is required. We will propose a synthesis of such model by means of Genetic Programming and we will compare our results with the state-of-the-art.XXVI Ciclo198

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

PSA 2020

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2020