Multi-Agent Algorithms for Collective Behavior: A structural and application-focused atlas
arXiv:2103.11067
Abstract
The goal of this paper is to provide a survey and application-focused atlas of collective behavior coordination algorithms for multi-agent systems. We survey the general family of collective behavior algorithms for multi-agent systems and classify them according to their underlying mathematical structure. In doing so, we aim to capture fundamental mathematical properties of algorithms (e.g., scalability with respect to the number of agents and bandwidth use) and to show how the same algorithm or family of algorithms can be used for multiple tasks and applications. Collectively, this paper provides an application-focused atlas of algorithms for collective behavior of multi-agent systems, with three objectives: 1. to act as a tutorial guide to practitioners in the selection of coordination algorithms for a given application; 2. to highlight how mathematically similar algorithms can be used for a variety of tasks, ranging from low-level control to high-level coordination; 3. to explore the state-of-the-art in the field of control of multi-agent systems and identify areas for future research.
Under review for journal publication. Revised and extended version of arXiv:1803.05464
References in corpus (9)
- Constrained Consensus
- Tracking control for multi-agent consensus with an active leader and variable topology
- Synchronization in Complex Oscillator Networks and Smart Grids
- Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis
- Likelihood Consensus and Its Application to Distributed Particle Filtering
- Stable Concurrent Synchronization in Dynamic System Networks
- Exploiting Subgraph Structure in Multi-Robot Path Planning
- Distributed and Adaptive Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment
- Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward