Evolving intelligent text-based agents

Abstract

In this paper we describe our neuro-genetic approach to developing a multi-agent system (MAS) which forages as well as meta-searches for multi-media information in online information sources on the ever-changing World Wide Web. We present EVA, an intelligent agent system that supports 1) multiple Web agents working together concurrently and collaboratively to achieve their common goal, 2) the evolution of these Web agents and the user profiles to achieve a better filtering, classification, and categorization performance, and 3) longer-term adaptation by using our unique neuro-genetic algorithm. Individual Web agents use neural networks for local searching and learning. Genetic algorithms are used to facilitate the evolution of agents on a global scale. NLP technology allows users to write sophisticated queries, and allows the system to extract important information from the user queries and the retrieved documents. The new text categorization technology used by EVA, which is also based on the neuro-genetic algorithm, can learn to automatically categorize and classify Web pages with high accuracy, using as few terms as possible. Additionally, we have developed a technique for integrating meta-searching and Web-crawling to produce intelligent agents that can retrieve documents more efficiently, and a self-feedback or automatic relevance feedback mechanism to automatically train the Web agents, without human intervention. This algorithm, together with the neuro-genetic algorithm, has greatly enhanced the autonomy of the Web agents.