Intelligent News Foragers: Efficient Information Discovery in a Scale-Free Web Environment

Title: Intelligent News Foragers: Efficient Information Discovery in a Scale-Free Web Environment

Research Question: How can intelligent agents efficiently discover and share novel information in a scale-free web environment where traps are abundant?

Methodology: The researchers developed an artificial life method with intelligent individuals, called news foragers, to detect 'breaking news' type information on a prominent and vast web domain. These foragers crawled the web by estimating the long-term cumulated reward using reinforcement learning. They used a probabilistic term-frequency inverse document-frequency (PrTFIDF) classifier and a well-known text clustering method to provide inputs to the function approximator.

Results: The experimental results showed that the foragers formed an artificial life community with no direct communication between them but a centralized rewarding system. The community achieved fast division of labor and efficient information discovery, even in the presence of traps.

Implications: This research suggests a novel approach to information discovery in scale-free web environments. The method demonstrates how intelligent agents can efficiently collaborate and share information, even in situations where direct communication is lacking. This could have significant implications for search engine algorithms, web crawling techniques, and the development of intelligent systems that operate in complex and dynamic environments.

Link to Article: https://arxiv.org/abs/0308042v1 Authors: arXiv ID: 0308042v1