Our paper, An Architecture for Identifying Emergent Behavior in Multi-Agent Systems has been accepted for publication at AAMAS 2014!

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 both new and previously defined in the literature. These can include among others agent interaction and statistical complexity, but our architecture is designed to easily include others. 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. Towards this, we advance an emergence taxonomy and a behavior repository. We instantiate our architecture using proposed interaction metrics and Hausdorff distances. Our experiments with multi-agent systems of various types and sizes show the feasibility of our approach and highlight future avenues of improvement.