Expected Value of Communication for Planning in Ad Hoc Teamwork
This work addresses the challenge of efficient coordination in multi-agent systems for applications like robotics or gaming, but it is incremental as it builds on existing ad hoc teamwork research.
The paper tackles the problem of autonomous agents coordinating with unknown teammates in ad hoc teamwork, where communication is costly, by proposing a new metric (Expected Divergence Point) and a planning algorithm to balance observation-based and communication-based plan recognition, demonstrating effectiveness in various scenarios.
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.