Towards Run-Time Search for Real-World Multi-Agent Systems

TL;DR

This paper explores the use of search heuristics to enhance run-time testing of multi-agent systems, aiming to better handle uncertainties like environmental changes and agent interactions through an experimental testbed.

Contribution

It proposes a novel approach of integrating search heuristics into run-time testing for MAS and discusses the development of an experimental testbed for this purpose.

Findings

Initial experimental testbed developed

Anticipated challenges identified for domain implementation

Potential for improved robustness in MAS testing

Abstract

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.