An architecture for identifying emergent behavior in multi-agent systems

Abstract

Multi-agent systems exhibit unexpected, emergent behavior as a result of the complexity of agent behaviors and their interactions. Despite significant research interest in the past decades, computational methods to identify and analyze emergence as it happens are still needed. This paper proposes a software architecture for identifying emergent behavior in a multi-agent system as it happens, using interval-based snapshots and emergent behavior metrics. We propose various distance functions to compare between the multi-agent system under analysis and systems that have been previously shown to exhibit emergent behavior.