ABSA: An Agent-Based Tool for System Administration

Studies indicate that because of the difficulty and complexity, the cost of administering systems is ten times the cost of the actual hardware. ABSA is an agent-based solution to automated system administration. ABSA architecture is introduced to minimize the cost of administering computers in multi-platform networks and to provide a simple, consistent, expandable, and integrated system administration tool

A Perception Based, Domain Specific Expert System for Question-Answering Support

The current search engine technologies mostly use a keyword based searching mechanism, which does not have any deductive abilities. There is an urgent need for a more intelligent question-answering system that will provide a more intuitive, natural language interface, and more accurate and direct search results. The introduction of Computing with Words (CwW) provides a new theoretical base for developing frameworks with support for dealing with information in natural language. This paper proposes a domain specific question-answering system based on Fuzzy Expert Systems using CwW. In order to perform the translation of natural language based information into a standard format for use with CwW, Probabilistic Context-Free Grammar is used

Preana: Game Theory Based Prediction with Reinforcement Learning

In this article, we have developed a game theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita’s algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players’ reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple steak-holders who have conflicting interests in economic, business, and political sciences. We have utilized game theory, expected utility theory, Median voter theory, probability distribution and reinforcement learning. We were able to reproduce Mesquita’s reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Irans 2013 presidential election to verify the accuracy of the prediction made by Preana

Parallel Computing with a Bayesian Item Response Model

Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computationally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably

A Multi-Agent Architecture for Distributed Domain-Specific Information Integration

On both the public Internet and private Intranets, there is a vast amount of data available that is owned and maintained by different organizations, distributed all around the world. These data resources are rich and recent; however, information gathering and knowledge discovery from them, in a particular knowledge domain, confronts major difficulties. The objective of this article is to introduce an autonomous methodology to provide for domain-specific information gathering and integration from multiple distributed sources

The Role of Particle Surface Functionality and Microstructure Development in Isothermal and Non-Isothermal Crystallization Behavior of Polyamide 6/Cellulose Nanocrystals Nanocomposites

Polyamide 6 (PA6)/cellulose nanocrystal (CNC) and aminopropyl triethoxy silane (APS) - modified CNC nanocomposites were prepared by in situ anionic ring opening polymerization and subsequent melt extrusion. The morphological observation of these hybrid systems revealed that the non-modified nanocrystals developed a network-like fibrillar structure while the APS-modified CNCs were finely dispersed mostly as individual whiskers. The isothermal and non-isothermal crystallization kinetics was extensively studied with emphasis on the effects of CNC surface functionality and the subsequent microstructure development on crystallization behavior of these novel nanocomposite systems. The non-modified CNC particles with corresponding fibrillar microstructure were found significantly hinder the crystallization process and spherultic growth of polyamide 6 chains under both isothermal and non-isothermal conditions. On other hand, the surface modified cellulose nanocrystals with improved sub-micron dispersion enhance crystal nucleation in early stages of crystallization while imposing opposite effect in later stages of crystallization resulting in development of relatively smaller defective spherulitic structures

Radical Agent-based Approach for Intelligence Analysis

This paper presents a novel agent-based framework as a decision aid tool for intelligence analysis. This technology extends net-centric information processing and abstraction as well as fusion and multi-source integration strategies. Our information agents traverse and mediate disparate ontologies in different formats providing a foundation for semantic interoperability. The presented system provides knowledge discovery by accessing a large number of information sources in a particular domain and organizing them into a network of information agents. Each agent provides expertise on a specific topic by drawing on relevant information from other information agents in related knowledge domains. Unique advantages include net-centric scalability, principled information assurance, as well as ground breaking knowledge discovery in service of intelligence analysis