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Partner Capability Estimation for Task-Agnostic Ada... | AI Research

Key Takeaways

Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork introduces a method for autonomous agents to collaborate with unknown partners...
Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents.
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.

Paper AbstractExpand

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.

Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork introduces a method for autonomous agents to collaborate with unknown partners by inferring their hidden, task-invariant capabilities. Rather than assuming a partner’s skill level is known or fixed to a single task, this research treats teamwork as a joint planning problem where the agent learns what a partner can and cannot do, allowing it to adapt its own behavior across different tasks.

Modeling Partner Capabilities

The authors propose that an agent should represent its partner through "capabilities"—latent, task-invariant constraints that dictate which actions are feasible in a given state. By modeling these constraints, an agent can distinguish between a partner who is choosing a specific strategy and one who is physically or cognitively unable to perform a task. This allows the agent to allocate work more effectively, as it can predict which joint plans are likely to succeed based on the partner's inferred limitations.

The CE-CM Approach

The researchers introduce Capability Estimation via Contextual Models (CE-CM), an approximate Bayesian method that functions through a "simulate-and-compare" loop. After observing a joint trajectory, the agent samples candidate capability vectors and simulates the resulting behavior. It then compares these simulations to the actual observed interaction to refine its belief about the partner’s capabilities. This approach requires no population pre-training and can adapt to a partner online after only a few tasks.

Accounting for Human Diversity

Recognizing that humans often act sub-optimally or use diverse strategies, the authors developed an extension called CE-CM-Div. While the base CE-CM method compares observations against a single optimal planner trajectory, CE-CM-Div evaluates capability hypotheses against a set of diverse planner rollouts. In an offline study of 225 trajectories from 15 human participants, the researchers found that CE-CM-Div provided substantially more accurate capability estimates than the baseline, confirming that accounting for behavioral variability is necessary for effective human-AI teaming.

Performance and Limitations

Experiments conducted in a symbolic household setting (TidyUP) and a dynamic coordination simulator (Overcooked) indicate that the capability-based model successfully reduces infeasible action assignments and adapts to changes in partner behavior over time. However, the study notes that the effectiveness of these models is tied to the assumption that the agent can accurately simulate potential partner behaviors. The results suggest that while capability-based modeling is a promising, interpretable approach for task-agnostic adaptation, its success depends on the agent's ability to account for the inherent unpredictability of human partners.

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