Decentralized Self-Adaption With Epidemic Algorithms for Agent-Based Transportation

Abstract

We investigate epidemic algorithms to enable agent-based transporters to be self-adaptive to disturbances and avoid centralized communication mechanisms. We conduct simulation experiments of a shop floor to compare the ability of mobile agents with limited perception to deliver items and adapt to a randomly disturbed environment by communicating decentralized with epidemic algorithms, and centralized via blackboard and direct messages. For evaluation of their adaption, we measure their task performance and communication efficiency. We conclude that agent-based transportation with epidemic algorithms can self-adapt to a disturbed environment, can still perform close to centralized ones, and avoid monolithic components.