Efficient Querying for Cooperative Probabilistic Commitments

TL;DR

This paper introduces an efficient method for cooperative multiagent systems to find near-optimal commitments through strategic querying, leveraging structural properties to reduce computational effort while maintaining high quality solutions.

Contribution

It presents a novel greedy query composition approach with provable approximation bounds for selecting commitments in cooperative multiagent settings.

Findings

The method finds nearly optimal commitments faster than existing approaches.

Structural properties of commitment values enable efficient query strategies.

Empirical results demonstrate significant time savings with minimal loss in optimality.

Abstract

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, should choose a cooperative commitment to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully-selected commitment choices. We prove structural properties of the agents' values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, which we empirically show can find nearly optimal commitments in a fraction of the time methods that lack our insights require.