Abstract
In ad hoc teamwork, multiple agents need to collaborate without having knowledge about their teammates or their plans a priori. A common assumption in this research area is that the agents cannot communicate. However, just as two random people may speak the same language, autonomous teammates may also happen to share a communication protocol. This paper considers how such a shared protocol can be leveraged, introducing a means to reason about Communication in Ad Hoc Teamwork (CAT). The goal of this work is enabling improved ad hoc teamwork by judiciously leveraging the ability of the team to communicate. We situate our study within a novel CAT scenario, involving tasks with multiple steps, where teammates' plans are unveiled over time. In this context, the paper proposes methods to reason about the timing and value of communication and introduces an algorithm for an ad hoc agent to leverage these methods. Finally, we introduces a new multiagent domain, the tool fetching domain, and we study how varying this domain's properties affects the usefulness of communication. Empirical results show the benefits of explicit reasoning about communication content and timing in ad hoc teamwork.
| Original language | English |
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| Title of host publication | Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
| Editors | Christian Bessiere |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 254-260 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780999241165 |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Duration: 1 Jan 2021 → … |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| Volume | 2021-January |
| ISSN (Print) | 1045-0823 |
Conference
| Conference | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
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| Country/Territory | Japan |
| City | Yokohama |
| Period | 1/01/21 → … |
Bibliographical note
Funding Information: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), ARO (W911NF-19-2-0333), 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.
Publisher Copyright:
© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.