Communication-Aware Multi-Agent Metareasoning for Decentralized Task Allocation

Abstract

Metareasoning refers to reasoning about one's own decision making process. This paper considers metareasoning about the decision making process in multi-agent settings. We present a multiagent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level. Given a set of multi-agent task allocation algorithms, we synthesize a policy that prescribes the best algorithm to use among a predefined set of algorithms for a given communication level. Since each agent in the team runs the same policy, the team (or a part of the team) will collectively switch between task allocation algorithms as a function of the observed level of communication. We apply reactive synthesis to generate the policy from high-level specifications written in Linear Temporal Logic encoding the agents' switching behavior with respect to the state of the environment. We perform experiments in simulation to identify the best performing algorithms under different communication levels. The communication environment is modeled using the Rayleigh fading model and communication estimation is done through the exchange of heartbeat messages among agents. We test our metareasoning policy in three types of scenarios: search & rescue, fire monitoring, and ship protection scenarios. For each scenario, we demonstrate that our policy achieved better performance with respect to either max distance traveled, max number of transmitted messages or both compared to running any single algorithm.

Distributed Task Allocation Algorithms for Multi-Agent Systems With Very Low Communication

“…The work in the current paper differs from our previous work [5], [22], [23] in a number of ways. Most importantly, the current paper focuses on new algorithms for the special case where communication is very low-p ≪ 0.01 and potentially nonexistent-and does not consider metareasoning.…”

“…Herrmann [22] discusses the use of experimental data about collaborative algorithms to develop a metareasoning policy that changes the algorithm that an agent is running based on the expected performance in that scenario. Carillo et al [23] explores using a formal logic based policy for metareasoning about which task allocation algorithm should be used in environments in which communication quality evolves over time. The current paper does not consider metareasoning, but provides additional tools that can potentially be used by a metaresoning task allocation system (for example, when communication degrades to such an extent that agents are rarely able to communicate).…”

An Agent-Based Ship Firefighting Model

“…The use of MAS related to fire onboard is not yet sufficiently represented in the literature. We found three papers on this topic [59][60][61].…”

“…A multi-agent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level is presented in [61]. Their metareasoning policy was tested in three types of scenarios one of which is fire monitoring.…”

On the role and opportunities in teamwork design for advanced multi-robot search systems

“…In Carrillo et al (2021), communication-aware multi-agent metareasoning for decentralized task allocation is considered. Multiagent metareasoning refers to the ability of agents to reason about their decision-making.…”