研究团队提出了CE-CM和CE-CM-Div两种能力估计方法，能让AI在未知合作伙伴能力时快速适应，还考虑了人类行为多样性，看模拟和真人实验结果都挺有说服力的。
论文将临时团队合作（AHT）扩展至多任务场景，提出CE-CM方法，通过贝叶斯推断从少量任务中在线学习隐藏的合作伙伴能力向量。CE-CM采用仿真采样构建上下文多智能体MDP进行规划，无需群体预训练。针对人类行为不可预测性，CE-CM-Div通过多样化规划轨迹评估能力假设，在模拟实验中快速恢复隐藏能力并减少不可行动作分配。基于225条人类轨迹的离线研究显示，CE-CM-Div比基线CE-CM显著提升能力估计准确性。
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
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.