Hamilton's Rule for Enabling Altruism in Multi-Agent Systems

TL;DR

This paper applies Hamilton's rule to multi-agent systems, proposing a framework where agents decide on altruistic actions based on a new fitness measure, enhancing coordination in navigation tasks.

Contribution

It introduces a novel adaptation of Hamilton's rule for altruism in multi-agent systems, formalizes altruistic decision-making with a graph model, and demonstrates improved coordination through simulations.

Findings

Altruistic behaviors improve task efficiency.

Agent importance influences altruistic decisions.

Framework enhances multi-agent coordination.

Abstract

This paper explores the application of Hamilton's rule to altruistic decision-making in multi-agent systems. Inspired by biological altruism, we introduce a framework that evaluates when individual agents should incur costs to benefit their neighbors. By adapting Hamilton's rule, we define agent ``fitness" in terms of task productivity rather than genetic survival. We formalize altruistic decision-making through a graph-based model of multi-agent interactions and propose a solution using collaborative control Lyapunov functions. The approach ensures that altruistic behaviors contribute to the collective goal-reaching efficiency of the system. We illustrate this framework on a multi-agent way-point navigation problem, where we show through simulation how agent importance levels influence altruistic decision-making, leading to improved coordination in navigation tasks.