Autonomic Computing: the natural fusion of Soft Computing and Hard Computing

Abstract- Autonomic Computing is emerging as a significant new approach in the design of computing systems. Its overall goal is the creation of Self-Managing Systems. In order to achieve this, Hard and So3 Computing are required. The benefits from utilizing Soy Computing include their ability to handle imprecision, uncertainty and partial truth that is inherently present in any complex real world problem accompanied by the practicable benefits of Hard Computing namely the stability of highly predictable solutions and typically low computational burden. This paper motivates the proposition that the successful creation of Autonomic Systems requires a fusion of Soj? Computing and Hard Computing

Language and Compiler Support for Auto-Tuning Variable-Accuracy Algorithms

Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance optimization in situations where perfect precision is not necessary. To this end, programmers often use approximation algorithms, iterative methods, data resampling, and other heuristics. However, programming such variable accuracy algorithms presents difficult challenges since the optimal algorithms and parameters may change with different accuracy requirements and usage environments. This problem is further compounded when multiple variable accuracy algorithms are nested together due to the complex way that accuracy requirements can propagate across algorithms and because of the resulting size of the set of allowable compositions. As a result, programmers often deal with this issue in an ad-hoc manner that can sometimes violate sound programming practices such as maintaining library abstractions. In this paper, we propose language extensions that expose trade-offs between time and accuracy to the compiler. The compiler performs fully automatic compile-time and install-time autotuning and analyses in order to construct optimized algorithms to achieve any given target accuracy. We present novel compiler techniques and a structured genetic tuning algorithm to search the space of candidate algorithms and accuracies in the presence of recursion and sub-calls to other variable accuracy code. These techniques benefit both the library writer, by providing an easy way to describe and search the parameter and algorithmic choice space, and the library user, by allowing high level specification of accuracy requirements which are then met automatically without the need for the user to understand any algorithm-specific parameters. Additionally, we present a new suite of benchmarks, written in our language, to examine the efficacy of our techniques. Our experimental results show that by relaxing accuracy requirements, we can easily obtain performance improvements ranging from 1.1x to orders of magnitude of speedup

Diseño e implementación de software para la detección y eliminación de inconsistencias en bases de reglas de sistemas difusos e inserción de reglas en caso de incompletitud de la base de conocimiento

El trabajo de investigación propuesto en esta tesis corresponde al módulo de validación de la base de reglas de sistemas difusos e inserción de reglas en caso de incompletitud. Este módulo permite mejorar la calidad del conocimiento del sistema experto difuso, disminuir la complejidad computacional del mismo y facilitar la supervisión, adaptación y/o modificación estructural, en el caso de modelos borrosos aplicados a procesos con dinámicas variables o no lineales. Mediante la creación de Sistema difusos de forma heurística, aplicando los conceptos de la lógica difusa y los algoritmos ajustados para detectar anomalías en la base de conocimientos de los sistemas creados por un experto se asegura de la creación de sistemas que no tengan un grado de error muy bajo a la hora de hacer inferencia en los valores de salida. Así mismo este trabajo de investigación creo la opción de poder generar un modelo difuso a partir de los datos cargados en un archivo de datos de entrada, que ordena los datos de entrada identificando las variables de entrada y la variable de salida que por defecto se trabajó con una (1). La aplicación permite detectar clases haciendo particiones del antecedente y consecuente lo que ayudará a generar una base de reglas consistente y sin las anomalías más comunes en una base de reglas como son: Contradicciones, Redundancias, Subsumicion, etc. La aplicación permite además incrementar el número de conjuntos buscando minimizar el error inicial que se establece para que el modelo sea el inicial.Incluye bibliografí

The development of an intelligent decision support framework in the contact centre environment

In a time of fast growing technology and communication systems, it is very important for the industry and the corporations to develop new contact centre environment technologies for better customer contact requirements. The integration of contact centre (CC) into day-to-day organisational operations represents one of the most promising trends in the 21 st century economy. Whatever the nature or point of contact, customers want a seamless interaction throughout their experience with the company. Customers receive more personalised experience, while the company itself can now provide a consistent message across all customer interactions. Based on the literature studies and the research carried out within the contact centre industry through the case studies, the author identified the customer and advisor behavioural attributes along with demographic, experience and others that later are used to derive the categories. Clustering technique identified the categories for customers and advisors. From the initial set of categories, fuzzy expert system framework was derived which assigned a customer or advisor with the pre-defined set of categories. The thesis has proposed two novel frameworks for categorisation of customer and advisor within contact centres and development of intelligent decision support framework that displays the right amount of information to the advisor at the right time. Furthermore, the frameworks were validated with qualitative expert judgement from the experts at the contact centres and through a simulation approach. The research has developed a novel Soft Computing based fuzzy logic categorisation framework that categorises customer and advisor on the basis of their demographic, experience and behavioural attributes. The study also identifies the behavioural aspects of customer and advisor within CC environment and on the basis of categorisation framework, assigns each customer and advisor to that of a pre-defined category. The research has also proposed an intelligent decision support framework to identify and display the minimum amount of information required by an advisor to serve the customer in CC environment. The performance of the proposed frameworks is analysed through four case studies. In this way this research proposes a fully tested and validated set of categorisation and information requirement frameworks for dealing with customer and advisor and its challenges. The research also identifies future research directions in the relevant areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo