标题： 基于知识的推理与学习在临时团队协作中的部分可观察性 显示英文标题

标题： Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork

摘要： 临时团队合作指的是使智能体在没有事先协调的情况下与队友协作的问题。数据驱动的方法是临时团队合作的最先进技术。它们使用大量标记的先前观察数据集来模拟其他智能体类型的行为，并确定临时智能体的行为。这些方法计算成本高，缺乏透明度，并且难以适应之前未见过的变化，例如在团队组成方面的变化。我们最近的工作引入了一种架构，该架构基于非单调逻辑推理，结合先验常识领域知识和从有限示例中学习到的其他智能体行为的预测模型，来确定临时智能体的行为。在本文中，我们大幅扩展了该架构的功能，以支持：(a) 对预测其他智能体行为的模型进行在线选择、适应和学习；以及 (b) 在部分可观测性和有限通信的情况下与队友协作。我们在两个模拟的多智能体基准领域中展示了并实验评估了我们架构的能力：Fort Attack 和 Half Field Offense。我们证明，在简单和复杂场景中，我们架构的性能与最先进的数据驱动基线相当或更好，特别是在有限训练数据、部分可观测性和团队组成变化的情况下。 显示英文摘要

摘要： Ad hoc teamwork refers to the problem of enabling an agent to collaborate with teammates without prior coordination. Data-driven methods represent the state of the art in ad hoc teamwork. They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes, e.g., in team composition. Our recent work introduced an architecture that determined an ad hoc agent's behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and predictive models of other agents' behavior that were learned from limited examples. In this paper, we substantially expand the architecture's capabilities to support: (a) online selection, adaptation, and learning of the models that predict the other agents' behavior; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate the capabilities of our architecture in two simulated multiagent benchmark domains for ad hoc teamwork: Fort Attack and Half Field Offense. We show that the performance of our architecture is comparable or better than state of the art data-driven baselines in both simple and complex scenarios, particularly in the presence of limited training data, partial observability, and changes in team composition.