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Source note3 min read

Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork

Source

arXiv

arxiv.org/abs/2607.27177v1

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Source

Plain-Language Brief

Researchers propose an approach called CE-CM (Capability Estimation via Contextual Models) to help autonomous agents collaborate with diverse partners in a multi-task setting. The method infers task-invariant capability vectors and uses simulation-based sampling to estimate capabilities and induce a contextual Multi-agent Markov Decision Process for planning. CE-CM-Div, an extension of this approach, accounts for human unpredictability by evaluating capability hypotheses against diverse planner rollouts. Simulated experiments show that CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts to changes over time. In an offline human study, CE-CM-Div significantly improved capability estimates compared to the baseline method.

What the Document Says

  • The research is about partner capability estimation for task-agnostic adaptation in ad-hoc teamwork.
  • CE-CM (Capability Estimation via Contextual Models) is an approximate Bayesian method that infers task-invariant capability vectors and uses simulation-based sampling to estimate capabilities.
  • The approach requires no population pre-training and refines its beliefs online from just a few tasks.
  • CE-CM-Div, an extension of CE-CM, evaluates capability hypotheses against diverse planner rollouts instead of a single optimal trajectory.
  • Simulated experiments demonstrate that CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts to changes over time.
  • In an offline human study of 225 trajectories from 15 participants, CE-CM-Div substantially improved capability estimates over the baseline CE-CM method.
  • The results suggest that capability-based modelling is a promising interpretable and task-agnostic representation in the studied settings.

Key Concepts Mentioned

  • Autonomous agents
  • Ad-hoc teamwork
  • Partner capabilities
  • Capability Estimation via Contextual Models (CE-CM)
  • Simulation-based sampling
  • Multi-agent Markov Decision Processes
  • Human unpredictability
  • Offline human study

Claims Made by the Document

  • CE-CM and CE-CM-Div can help autonomous agents collaborate with diverse partners in a multi-task setting.
  • CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts to changes over time.

Evidence Used

  • Simulated experiments and an offline human study are cited as evidence for the claims.

Methods / Approach

  • CE-CM uses approximate Bayesian inference and simulation-based sampling.
  • CE-CM-Div evaluates capability hypotheses against diverse planner rollouts.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The data provided does not explicitly state any limitations by the authors. However, as an assistant, it's worth noting that the effectiveness of CE-CM and CE-CM-Div may vary in real-world settings due to factors such as differences in partner behavior compared to simulation results.

Open Questions

  • How well does CE-CM and CE-CM-Div perform in real-world scenarios with varying degrees of complexity, unpredictability, and human participation?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The research focuses on the performance of CE-CM and CE-CM-Div in a multi-task setting for ad-hoc teamwork. It suggests that these methods improve the estimation of partner capabilities and adapt to changes over time, but their real-world effectiveness may vary.

Raw Abstract / Excerpt

Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents. Most current ad-hoc teamwork (AHT) approaches assume that agents will collaborate on a single, fixed task and that the partner's capabilities, their ability to successfully execute the desired action, are already known. In reality, a partner's true capabilities are often hidden, and human collaborators may act sub-optimally on tasks with multiple valid strategies. To address these limitations, we extend ad-hoc teamwork into a multi-task setting by re-framing it as a problem of joint planning with decentralised execution under hidden partner capabilities. We introduce CE-CM (Capability Estimation via Contextual Models), an approximate Bayesian method that infers task-invariant capability vectors. By using simulation-based sampling, the agent estimates capabilities and induces a contextual Multi-agent Markov Decision Processes for planning. This approach requires no population pre-training and refines its beliefs online from just a few tasks. To account for human unpredictability, we propose CE-CM-Div, an extension that evaluates capability hypotheses against diverse planner rollouts rather than a single optimal trajectory. Simulated experiments demonstrate that CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts to changes over time. Furthermore, in an offline human study of 225 trajectories from 15 participants, CE-CM-Div substantially improved capability estimates over the baseline CE-CM method. Our results suggest capability-based modelling is a promising interpretable, task-agnostic representation in the studied settings, demonstrating that accounting for behavioural diversity is essential for robust human-AI teaming.