MacroSwarm: A Field-Based Compositional Framework for Swarm Programming

2024-01-19

International Conference on Coordination Models and Languages

AI Summary

Collective behavior engineering lacks general, systematic methodologies for specifying complex global objectives—such as pattern formation, collective motion, or distributed sensing—in swarm systems. Method: This paper introduces MacroSwarm, a novel macroprogramming framework that establishes a field-based programming paradigm grounded in the mapping between perception fields and actuation fields. It enables pure-functional specification, reuse, and composition of modular, field-driven behavioral primitives. The framework incorporates self-stabilizing field computation to ensure robustness and tightly integrates macroprogramming with systematic swarm system design. Contribution/Results: Leveraging field calculus and functional behavioral abstraction, MacroSwarm demonstrates high expressiveness and practicality across diverse simulations. Its self-stabilization property is formally proven. The approach significantly enhances modularity and systemic resilience in collective behavior design, advancing the engineering rigor of swarm systems.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.

Problem

• Develops a field-based framework for swarm behavior programming
• Enables reusable composable blocks for collective computation
• Demonstrates swarm patterns like flocking and decision-making

Innovation

• Field-based coordination for swarm behavior
• Reusable composable functional blocks
• Macroprogramming with pure functions