Expected Value of Communication for Planning in Ad Hoc Teamwork

TL;DR

This paper introduces a new metric and planning algorithm for ad hoc teamwork where agents communicate at a cost, balancing observation-based recognition and communication to improve coordination.

Contribution

It proposes the Expected Divergence Point (EDP) metric and a novel planning algorithm for cost-aware communication in ad hoc teamwork scenarios.

Findings

The EDP metric effectively measures policy similarity.

The planning algorithm improves coordination efficiency.

Communication strategies adapt to different cost scenarios.

Abstract

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.