Implementación de sistemas fuzzy complejos sobre FPGAs

Las desventajas de las soluciones hardware dedicadas para la implementación de sistemas de inferencia fuzzy cuando se comparan con las estrategias basadas en software son principalmente la falta de flexibilidad y la complicación en el proceso de diseño. En este trabajo se presenta una arquitectura novedosa que permite la síntesis electrónica y la implementación hardware de sistemas expertos basados en conocimiento fuzzy. La definición de la arquitectura se basa en la descripción en forma de red de Petri de la base de reglas complejas, heredando de ella las características de modularidad y escalabilidad. Los componentes de nuestra arquitectura se definen entonces utilizando descripciones VHDL de alto nivel. Por ello, nuestra metodología de diseño proporciona flexibilidad, reusabilidad e independencia tanto de la tecnología electrónica como del tipo y tamaño de la aplicación, solucionando la mayoría de las limitaciones del hardware fuzzy

Approximate syllogistic reasoning: a contribution to inference patterns and use cases

In this thesis two models of syllogistic reasoning for dealing with arguments that involve fuzzy quantified statements and approximate chaining are proposed. The modeling of quantified statements is based on the Theory of Generalized Quantifiers, which allows us to manage different kind of quantifiers simultaneously, and the inference process is interpreted in terms of a mathematical optimization problem, which allows us to deal with more arguments that standard deductive ones. For the case of approximate chaining, we propose to use synonymy, as used in a thesaurus, for calculating the degree of confidence of the argument according to the degree of similarity between chaining terms. As use cases, different types of Bayesian reasoning (Generalized Bayes' Theorem, Bayesian networks and probabilistic reasoning in legal argumentation) are analysed for being expressed through syllogisms

CP-nets: From Theory to Practice

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data

Harold Garfinkel: Studies of Work in the Sciences

This volume includes an unpublished manuscript and selected portions of five seminars by Harold Garfinkel – the founder of ethnomethodology – on the topic of practices in the natural sciences and mathematics. The volume provides a coherent and sustained account of his program for the study of ordinary and specialized social actions. Presenting broader theoretical and methodological initiatives, as well as discussions and summaries of exemplary studies of social phenomena within and beyond the sciences, this work dates to the period in the 1980s during which the field of Science and Technology Studies was taking shape, with ethnomethodological studies of scientific practice forming a major part of its development at the time. Aside from their historical importance, the manuscript and seminars present a distinctive perspective on the natural and social sciences that remains highly original and pertinent to research on science, social science, and everyday life today. Offering critical insights and proposals relating to developments in Ethnomethodology and Conversation Analysis, this volume will appeal to scholars of Sociology and Science and Technology Studies with interests in the work of Garfinkel

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions

Harold Garfinkel: Studies of Work in the Sciences

This volume includes an unpublished manuscript and selected portions of five seminars by Harold Garfinkel – the founder of ethnomethodology – on the topic of practices in the natural sciences and mathematics. The volume provides a coherent and sustained account of his program for the study of ordinary and specialized social actions. Presenting broader theoretical and methodological initiatives, as well as discussions and summaries of exemplary studies of social phenomena within and beyond the sciences, this work dates to the period in the 1980s during which the field of Science and Technology Studies was taking shape, with ethnomethodological studies of scientific practice forming a major part of its development at the time. Aside from their historical importance, the manuscript and seminars present a distinctive perspective on the natural and social sciences that remains highly original and pertinent to research on science, social science, and everyday life today. Offering critical insights and proposals relating to developments in Ethnomethodology and Conversation Analysis, this volume will appeal to scholars of Sociology and Science and Technology Studies with interests in the work of Garfinkel

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Towards Lifelong Reasoning with Sparse and Compressive Memory Systems

Humans have a remarkable ability to remember information over long time horizons. When reading a book, we build up a compressed representation of the past narrative, such as the characters and events that have built up the story so far. We can do this even if they are separated by thousands of words from the current text, or long stretches of time between readings. During our life, we build up and retain memories that tell us where we live, what we have experienced, and who we are. Adding memory to artificial neural networks has been transformative in machine learning, allowing models to extract structure from temporal data, and more accurately model the future. However the capacity for long-range reasoning in current memory-augmented neural networks is considerably limited, in comparison to humans, despite the access to powerful modern computers. This thesis explores two prominent approaches towards scaling artificial memories to lifelong capacity: sparse access and compressive memory structures. With sparse access, the inspection, retrieval, and updating of only a very small subset of pertinent memory is considered. It is found that sparse memory access is beneficial for learning, allowing for improved data-efficiency and improved generalisation. From a computational perspective - sparsity allows scaling to memories with millions of entities on a simple CPU-based machine. It is shown that memory systems that compress the past to a smaller set of representations reduce redundancy and can speed up the learning of rare classes and improve upon classical data-structures in database systems. Compressive memory architectures are also devised for sequence prediction tasks and are observed to significantly increase the state-of-the-art in modelling natural language