提出可预测动态变化的信念模型，提升多智能体规划适应性
Beyond Static Assumptions: the Predictive Justified Perspective Model for Epistemic Planning
- 基于历史观测预测环境变量变化，突破静态假设限制
- 在多个典型场景中表现优于传统模型，验证了泛化能力
- 适合需动态推理的机器人协同任务，如自动驾驶、无人机编队
认知规划（Epistemic Planning, EP）致力于在多智能体协作或对抗环境中推理智能体的知识与信念。当前最先进的方法是合理视角（Justified Perspective, JP）模型，兼具高效性与表达力。然而，现有所有EP方法均继承经典规划的静态环境假设，限制了其在含动态变量的机器人场景中的应用。本文提出一种JP模型的扩展——预测合理视角（Predictive Justified Perspective, PJP）模型，以消除该假设。PJP不再假设信念自上次观测后保持不变，而是利用全部历史观测对变化变量进行预测。文中给出了预测函数的定义并提供示例，证明其可支持任意嵌套结构。我们在多个经典领域实现并测试了PJP模型，实验表明其在不同场景下均显著优于JP模型，展现出在机器人等实际应用中提升认知规划性能的巨大潜力。
原文摘要 · Abstract (English)
Epistemic Planning (EP) is an important research area dedicated to reasoning about the knowledge and beliefs of agents in multi-agent cooperative or adversarial settings. The Justified Perspective (JP) model is the state-of-the-art approach to solving EP problems with efficiency and expressiveness. However, all existing EP methods inherit the static environment assumption from classical planning. This limitation hinders the application of EP in fields such as robotics with multi-agent settings, where the environment contains changing variables. In this paper, we propose an extension of the JP model, namely, the Predictive Justified Perspective (PJP) model, to remove this assumption. Instead of assuming that beliefs remain unchanged since the last observation, the PJP model uses all past observations to form predictions about the changing variables. The definition of the prediction function with examples is provided, and it is demonstrated that it can work with arbitrary nesting. We then implemented the PJP model in several well-known domains and compared it with the JP model in the experiments. The results indicated that the PJP model performs exceptionally well across various domains, demonstrating its potential in improving EP applications in robotics.
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