Topology Estimation for Open Multi-Agent Systems

TL;DR

This paper introduces a robust method for estimating the interaction topology in open multi-agent systems with rapidly changing interactions and dynamic node sets.

Contribution

It proposes a projection-based dissimilarity measure and clustering approach that improves topology reconstruction in fast-switching, dynamic multi-agent environments.

Findings

The method accurately reconstructs interaction topology in simulations.

It outperforms traditional segment-wise estimation methods.

The approach is robust to rapid interaction changes.

Abstract

We address the problem of interaction topology identification in open multi-agent systems (OMAS) with dynamic node sets and fast switching interactions. In such systems, new agents join and interactions change rapidly, resulting in intervals with short dwell time and rendering conventional segment-wise estimation and clustering methods unreliable. To overcome this, we propose a projection-based dissimilarity measure derived from a consistency property of local least-squares operators, enabling robust mode clustering. Aggregating intervals within each cluster yields accurate topology estimates. The proposed framework offers a systematic solution for reconstructing the interaction topology of OMAS subject to fast switching. Finally, we illustrate our theoretical results via numerical simulations.