Emergent Collective Behavior in Multi-Agent Systems: An Evolutionary Perspective

Abstract

The study of collective behavior involves the analysis of interactions among a set of agents that yield collective outcomes at the level of the group. The behavior is said to be emergent when it cannot be understood simply as the sum of its constituent parts. Further, group-level outcomes can in turn influence individual interactions. The complexity of this interplay makes the study of emergence challenging and exciting. This dissertation is focused on the study of emergent collective behavior from the perspective of evolution. Evolution is a simple yet powerful algorithm, which when acting on interacting entities in a dynamic environment, yields an array of fascinating behavior as manifest in the natural world. Natural collectives display a wide variety of cooperative behavior and have evolved to efficiently manage the inherent tradeoff between robust behavior and adaptability to dynamic environments. These properties have motivated the design of bio-inspired algorithms for sensing and decision-making in robotic collectives. In this work, we study the evolutionary mechanisms for cooperation and tradeoff management in biological collectives, with a focus on four related topics: replicator-mutator dynamics, collective migration, collective pursuit