Mission Level Uncertainty in Multi-Agent Resource Allocation

TL;DR

This paper investigates how local information inconsistencies among agents affect multi-agent system performance and proposes utility design methods to optimize collective behavior despite uncertainty.

Contribution

It introduces a utility design framework for multi-agent systems under information uncertainty, improving efficiency in set covering games.

Findings

Underestimating uncertainty improves price of anarchy.

Designed utilities optimize collective efficiency.

Information inconsistencies impact system performance.

Abstract

In recent years, a significant research effort has been devoted to the design of distributed protocols for the control of multi-agent systems, as the scale and limited communication bandwidth characteristic of such systems render centralized control impossible. Given the strict operating conditions, it is unlikely that every agent in a multi-agent system will have local information that is consistent with the true system state. Yet, the majority of works in the literature assume that agents share perfect knowledge of their environment. This paper focuses on understanding the impact that inconsistencies in agents' local information can have on the performance of multi-agent systems. More specifically, we consider the design of multi-agent operations under a game theoretic lens where individual agents are assigned utilities that guide their local decision making. We provide a tractable procedure for designing utilities that optimize the efficiency of the resulting collective behavior (i.e., price of anarchy) for classes of set covering games where the extent of the information inconsistencies is known. In the setting where the extent of the informational inconsistencies is not known, we show -- perhaps surprisingly -- that underestimating the level of uncertainty leads to better price of anarchy than overestimating it.