Automatic evolution of ideas through multilayer evolutionary system to support creative thinking

Creativity is a tool that helps in effective problem solving utilizing optimum scarce resources in any business. This paper presents a conceptual framework of a multilayer evolutionary system that supports creative thinking. The system evolves, using a genetic algorithm, new ideas from a set of basic ideas that are casually provided through an interactive editor or selected from past transaction records. The architecture proposed here encompasses three layers called system layer, database layer, and query producer and user interface layer. Besides the general architecture, the paper also describes the detailed methodology, genetic procedure to evolve ideas, reproduction operators like modified mutation, cross over and selection; and fitness functions to evolve suitable and strong ideas. The system layer further describes algorithm of stimulus-generation process. The proposed architecture is easy to develop, generic, domain independent and works with databases, which increases scope and usability of the system. Above all, the interactive user interface makes the system friendly and easy to operate

Development of decision support system for the diagnosis of arthritis pain for rheumatic fever patients: Based on the fuzzy approach

Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules

Multi-Agent System for Knowledge-Based Access to Distributed Databases

A framework for knowledge discovery, knowledge use, and knowledge management is presented in this article to provide knowledge-based access of the domain databases using multi-agent systems approach. This framework encompasses five different agents: namely, knowledge management agent, data filter agent, rule induction agent, dynamic analysis agent, and interface agent. This article suggests an enhancement in the typical Knowledge Query and Manipulation Language (KQML) used to interact recurrently and to share information between multiple agents to achieve their goals by including the notion of linguistic variable and, hence, to support fuzzy decision making. The article also includes a sample KQML query block (along with membership function used by the knowledge management agent), result of the query, and structure of database files for a co-operative dairy. The approach provides advantages like effectiveness, explanation, reasoning, multimedia, and user-friendly interface in accessing multiple databases for an application