Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling

TL;DR

This paper integrates cooperative game theory with multi-agent systems to develop self-organizing agents that achieve optimal real-time scheduling in large-scale, real-world environments through emergent behavior.

Contribution

It introduces a novel approach combining game theory and multi-agent systems to optimize scheduling in large-scale real-time systems.

Findings

Achieved near-optimal real-time scheduling in simulations

Demonstrated emergent coordination among thousands of agents

Validated approach in a High Energy Physics experiment setting

Abstract

Research concerning organization and coordination within multi-agent systems continues to draw from a variety of architectures and methodologies. The work presented in this paper combines techniques from game theory and multi-agent systems to produce self-organizing, polymorphic, lightweight, embedded agents for systems scheduling within a large-scale real-time systems environment. Results show how this approach is used to experimentally produce optimum real-time scheduling through the emergent behavior of thousands of agents. These results are obtained using a SWARM simulation of systems scheduling within a High Energy Physics experiment consisting of 2500 digital signal processors.