Using Collective Intelligence to Route Internet Traffic
arXiv:cs/9905004
Abstract
A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.
7 pages
Cited by in corpus (10)
- An Introduction to Collective Intelligence
- Collective Intelligence for Control of Distributed Dynamical Systems
- Collective Intelligence, Data Routing and Braess' Paradox
- Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
- Distributed Constrained Optimization with Semicoordinate Transformations
- Avoiding Braess' Paradox through Collective Intelligence
- General Principles of Learning-Based Multi-Agent Systems
- Are Multiagent Systems Resilient to Communication Failures?
- A Predictive Theory of Games
- Collectives for the Optimal Combination of Imperfect Objects