Centralized reward system gives rise to fast and efficient work sharing for intelligent Internet agents lacking direct communication

TL;DR

This paper introduces a centralized reward system for intelligent Internet agents that enables fast and efficient work sharing without direct communication, transforming traps in scale-free networks into valuable information repositories.

Contribution

A novel centralized reward mechanism for intelligent agents that promotes rapid work sharing and community growth without direct agent communication.

Findings

Agents efficiently discover novel information in scale-free networks.

The community exhibits rapid division of labor and growth.

The system effectively transforms network traps into information sources.

Abstract

WWW has a scale-free structure where novel information is often difficult to locate. Moreover, Intelligent agents easily get trapped in this structure. Here a novel method is put forth, which turns these traps into information repositories, supplies: We populated an Internet environment with intelligent news foragers. Foraging has its associated cost whereas foragers are rewarded if they detect not yet discovered novel information. The intelligent news foragers crawl by using the estimated long-term cumulated reward, and also have a finite sized memory: the list of most promising supplies. Foragers form an artificial life community: the most successful ones are allowed to multiply, while unsuccessful ones die out. The specific property of this community is that there is no direct communication amongst foragers but the centralized rewarding system. Still, fast division of work is achieved.