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.
| Original language | English |
|---|---|
| Title of host publication | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Pages | 11290-11298 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781713835974 |
| State | Published - 2021 |
| Externally published | Yes |
| Event | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021 |
Publication series
| Name | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
|---|---|
| Volume | 13A |
Conference
| Conference | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
|---|---|
| City | Virtual, Online |
| Period | 2/02/21 → 9/02/21 |
Bibliographical note
Publisher Copyright:Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
This work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (CPS-1739964, IIS-1724157, NRI-1925082), ONR (N00014-18-2243), FLI (RFP2-000), ARL, DARPA, Lockheed Martin, GM, and Bosch. Peter Stone serves as the Executive Director of Sony AI America and receives financial compensation for this work. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research.
| Funders | Funder number |
|---|---|
| National Science Foundation | IIS-1724157, NRI-1925082, CPS-1739964 |
| Office of Naval Research | N00014-18-2243 |
| Defense Advanced Research Projects Agency | |
| Army Research Laboratory | |
| University of Texas at Austin | |
| Robert Bosch (Australia) Pty | |
| Future of Life Institute | RFP2-000 |