发表机构
King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对临时团队协作中伙伴能力隐藏与人类行为不可预测问题，提出CE-CM及CE-CM-Div方法，通过仿真采样与多样规划器rollout实现任务无关的能力估计，提升了团队协作的可行性与鲁棒性。
AI 中文摘要
与新颖多样的伙伴开展有效协作是自主智能体的关键技能。当前大多数临时团队协作（Ad-Hoc Teamwork, AHT）方法假设智能体将在单一固定任务上协作，且伙伴的能力（其成功执行期望动作的能力）已被知晓。但现实中，伙伴的真实能力往往是隐藏的，人类协作者在拥有多种有效策略的任务上可能表现欠佳。为解决这些局限，我们将临时团队协作扩展至多任务场景，将其重新定义为在伙伴能力隐藏条件下、去中心化执行的联合规划问题。我们提出CE-CM（基于上下文模型的能力估计），这是一种近似贝叶斯方法，用于推断任务无关的能力向量。通过基于仿真的采样，智能体估计能力并生成上下文多智能体马尔可夫决策过程用于规划。该方法无需群体预训练，仅从少量任务中在线修正信念。为应对人类的不可预测性，我们提出CE-CM-Div，其扩展方案通过多样的规划器rollout而非单一最优轨迹评估能力假设。仿真实验表明，CE-CM可快速恢复隐藏能力、减少不可行动作分配并适应随时间的变化。此外，在针对15名参与者的225条轨迹的离线人类研究中，CE-CM-Div相比基线CE-CM大幅提升了能力估计效果。我们的结果表明，基于能力的建模在研究场景中是一种有前景的可解释、任务无关表示，证明考虑行为多样性对稳健的人机协作至关重要。
英文摘要
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.
Comments44 pages, 18 figures, submitted
