Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork· 即兴团队协作中的伙伴能力估计
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 vector
提出了一种新型的伙伴能力估计方法，以适应多任务的即兴团队协作。
- 核心方法
- 引入CE-CM方法，通过上下文模型和近似贝叶斯推理来估计伙伴的能力向量，适应多任务环境。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有即兴团队协作方案大多假设任务单一固定，且已经了解伙伴的能力，实际中伙伴能力未知且可能因任务而多样化。
- 关键实验
- 在多个模拟环境中进行了实验，验证了CE-CM方法的有效性和适应性。
- 主要贡献
- 首次将即兴团队协作扩展至多任务场景，并提出了一种有效估计合作伙伴未知能力的方法。
- 意义与局限
- 对提升自主代理在未知伙伴和多任务场景下的协作能力具有重要意义，但模型的泛化能力和实时性能仍需进一步研究。