mlsanityData to 7 Oct 2026

Expected Value of Communication for Planning in Ad Hoc Teamwork

arXiv 2103.01171 ↗First posted 1 Mar 2021cs.AIcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

8 of 38 references.

References · 8

  1. 2016OpenAI Gymcited by 1,684
  2. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  3. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
  4. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 173
  5. 2017Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problemscited by 126
  6. 2020Too many cooks: Bayesian inference for coordinating multi-agent collaborationcited by 29
  7. 2019Reasoning about Hypothetical Agent Behaviours and their Parameterscited by 24
  8. 2019Active Goal Recognitioncited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 9; 2023: 4; 2024: 2; 2025: 2; 2026: 1.

19 papers.

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  1. 2022A Survey of Ad Hoc Teamwork Researchcited by 41
  2. 2023Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Gamecited by 27
  3. 2022Contrastive Identity-Aware Learning for Multi-Agent Value Decompositioncited by 19
  4. 2022A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learningcited by 12
  5. 2022Goal Recognition as Reinforcement Learningcited by 9
  6. 2022The Importance of Credo in Multiagent Learningcited by 7
  7. 2024Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racingcited by 7
  8. 2023Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)cited by 6
  9. 2022Exploring the Benefits of Teams in Multiagent Learningcited by 5
  10. 2021The Power of Communication in a Distributed Multi-Agent Systemcited by 4
  11. 2022Deep Reinforcement Learning for Multi-Agent Interactioncited by 3
  12. 2024Inverse Attention Agents for Multi-Agent Systemscited by 3
7 more
  1. 2022Toward multi-target self-organizing pursuit in a partially observable Markov gamecited by 2
  2. 2023Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamworkcited by 2
  3. 2022Ad Hoc Teamwork in the Presence of Adversariescited by 1
  4. 2023Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teamscited by 1
  5. 2026Robots Influencing Humans to Reveal their Goals during Collaboration and Competitioncited by 1
  6. 2025Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performancecited by 0
  7. 2025Generic-to-Specific Reasoning and Learning for Scalable Ad Hoc Teamworkcited by 0