Computing of Applied Digital Ecosystems

TL;DR

This paper explores how computing technologies like Multi-Agent Systems and evolutionary computing can enable digital ecosystems to exhibit self-organising, robust, and scalable properties inspired by biological ecosystems, supported by simulation results.

Contribution

It identifies specific computing technologies that contribute to self-organisation in digital ecosystems and demonstrates their effectiveness through experimental simulations.

Findings

Digital Ecosystems can self-organise and adapt to user requests.

Simulations show diversity correlates with user behavior.

Proposed architecture enhances robustness and scalability.

Abstract

A primary motivation for our research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. Here, we discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems, Service-Oriented Architectures, and distributed evolutionary computing. The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, considering the self-organised diversity of its evolving agent populations relative to the user request behaviour.