Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Emotional Intelligence and Organisational Role Stress — A study among managers in selected industrial organisations in Kerala

Emotional intelligence is very important in organisations and the ability to manage feelings and handle stress is an important aspect of El. Even though a number of studies have been done to prove that E1 is related to organisationally relevant variables like leadership effectiveness, job satisfaction, performance, career success etc., and the theoretical grounding for emotional intelligence-stress-relationship seems sound, only a few studies have been done to establish this linkage. This study is an attempt to measure emotional intelligence and organisational role stress of managers working in industrial organisations and to examine the relationship between Emotional Intelligence and Organisational Role Stress. It also attempts to explore the influence of personal and occupational variables viz., age, education, gender, marital status, experience, department, type of organisation and designation on emotional intelligence. The investigator has also examined the difference in the level of role stress experienced by junior, middle and senior-level managers. The main objective of the study is to examine the relationship between emotional intelligence and organisational role stress.

Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach.

Factorial Hidden Markov Models

We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Adaptive business intelligence for an open negotiation environment

Engineering of negotiation model allows to develop effective heuristic for business intelligence. Digital ecosystems demand open negotiation models. To define in advance effective heuristics is not compliant with the requirement of openness. The new challenge is to develop business intelligence in advance exploiting an adaptive approach. The idea is to learn business strategy once new negotiation model rise in the e-market arena. In this paper we present how recommendation technology may be deployed in an open negotiation environment where the interaction protocol models are not known in advance. The solution we propose is delivered as part of the ONE Platform, open source software that implements a fully distributed open environment for business negotiation

Methodology to obtain the user's Human Values Scale from Smart User Models

En años recientes,la Inteligencia Artificial ha contribuido a resolver problemas encontrados en el desempeño de las tareas de unidades informáticas, tanto si las computadoras están distribuidas para interactuar entre ellas o en cualquier entorno (Inteligencia Artificial Distribuida). Las Tecnologías de la Información permiten la creación de soluciones novedosas para problemas específicos mediante la aplicación de los hallazgos en diversas áreas de investigación. Nuestro trabajo está dirigido a la creación de modelos de usuario mediante un enfoque multidisciplinario en los cuales se emplean los principios de la psicología, inteligencia artificial distribuida, y el aprendizaje automático para crear modelos de usuario en entornos abiertos; uno de estos es la Inteligencia Ambiental basada en Modelos de Usuario con funciones de aprendizaje incremental y distribuido (conocidos como Smart User Model). Basándonos en estos modelos de usuario, dirigimos esta investigación a la adquisición de características del usuario importantes y que determinan la escala de valores dominantes de este en aquellos temas en los cuales está más interesado, desarrollando una metodología para obtener la Escala de Valores Humanos del usuario con respecto a sus características objetivas, subjetivas y emocionales (particularmente en Sistemas de Recomendación).Una de las áreas que ha sido poco investigada es la inclusión de la escala de valores humanos en los sistemas de información. Un Sistema de Recomendación, Modelo de usuario o Sistemas de Información, solo toman en cuenta las preferencias y emociones del usuario [Velásquez, 1996, 1997; Goldspink, 2000; Conte and Paolucci, 2001; Urban and Schmidt, 2001; Dal Forno and Merlone, 2001, 2002; Berkovsky et al., 2007c]. Por lo tanto, el principal enfoque de nuestra investigación está basado en la creación de una metodología que permita la generación de una escala de valores humanos para el usuario desde el modelo de usuario. Presentamos resultados obtenidos de un estudio de casos utilizando las características objetivas, subjetivas y emocionales en las áreas de servicios bancarios y de restaurantes donde la metodología propuesta en esta investigación fue puesta a prueba.En esta tesis, las principales contribuciones son: El desarrollo de una metodología que, dado un modelo de usuario con atributos objetivos, subjetivos y emocionales, se obtenga la Escala de Valores Humanos del usuario. La metodología propuesta está basada en el uso de aplicaciones ya existentes, donde todas las conexiones entre usuarios, agentes y dominios que se caracterizan por estas particularidades y atributos; por lo tanto, no se requiere de un esfuerzo extra por parte del usuario.

Opportunities for improvements in flocking models

Flocking is the capacity of coherent movement between multiple animals, including birds. Prominent research into flocking is presented. Particle Swarm Optimisation (PSO) has been the prominent result from research into flocking. It is considered that opportunities for further research in flocking exist. With the potential for automated traffic systems, it is concluded that flocking should be reinvestigated for this purpose.

Autonomous execution of tasks by swarm carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing system? The work reported in this paper is motivated towards bridging this gap by proposing swarm-array computing, a novel technique to achieve autonomy for distributed parallel computing systems. Among three proposed approaches, the second approach, namely 'Intelligent Agents' is of focus in this paper. The task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier. agents and can be seamlessly transferred between cores in the event of a pre-dicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed approach is validated on a multi-agent simulator.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

A mathematical model, implementation and study of a swarm system

The work reported in this paper is motivated towards the development of a mathematical model for swarm systems based on macroscopic primitives. A pattern formation and transformation model is proposed. The pattern transformation model comprises two general methods for pattern transformation, namely a macroscopic transformation method and a mathematical transformation method. The problem of transformation is formally expressed and four special cases of transformation are considered. Simulations to confirm the feasibility of the proposed models and transformation methods are presented. Comparison between the two transformation methods is also reported.

Intraplate earthquake swarm in Belo Jardim, NE Brazil: reactivation of a major Neoproterozoic shear zone (Pernambuco Lineament)

Intraplate earthquakes in stable continental areas have been explained basically by reactivation of pre-existing zones of weakness, stress concentration, or both. Zones of weakness are usually identified as sites of the last major orogeny, provinces of recent alkaline intrusions, or stretched crust in ancient rifts. However, it is difficult to identify specific zones of weakness and intraplate fault zones are not always easily correlated with known geological features. Although Northeastern Brazil is one of the most seismically active areas in the country (magnitudes 5 roughly every 5 yr), with hypocentral depths shallower than similar to 10 km and seismic zones as long as 30-40 km, no clear relationship with the known surface geology can be usually established with confidence, and a clear identification of zones of weakness has not yet been possible. Here we present the first clear case of seismic activity occurring as reactivation of an old structure in Brazil: the Pernambuco Lineament, a major Neoproterozoic shear zone. The 2004 earthquake swarm of Belo Jardim (magnitudes up to 3.1) and the recurrent activities in the nearby towns of Sao Caetano and Caruaru (magnitudes up to 4.0 and 3.8), show that the Pernambuco Lineament is a weak zone. A local seismic network showed that the Belo Jardim swarm of 2004 November occurred by normal faulting on a North dipping, E-W oriented fault plane in close agreement with the E-W trending structures within the Pernambuco Lineament. The Belo Jardim activity was concentrated in a 1.5 km (E-W) by 2 km (downdip) fault area, and average depth of 4.5 km. The nearby Caruaru activity occurs as both strike-slip and normal faulting, also consistent with local structures of the Pernambuco Lineament. The focal mechanisms of Belo Jardim, Caruaru and S. Caetano, indicate E-W compressional and N-S extensional principal stresses. The NS extension of this stress field is larger than that predicted by numerical models such as those of Coblentz & Richardson and we propose that additional factors such as flexural stresses from the nearby Sergipe-Alagoas marginal basin could also affect the current stress field in the Pernambuco Lineament.

Equipping Simulation Model (BIOSIM)’s Actors With Multi-agent Intelligence on Cross platforms

Thesis is to Introduce an Intelligent cross platform architecture with Multi-agent system in order to equip the simulation Models with agents, having intelligent behavior, reactive and pro-active nature and rational in decision making.