Recent approaches to goal recognition have leveraged planning landmarks to achieve high-accuracy with low runtime cost. These approaches, however, lack a probabilistic interpretation. Furthermore, while most probabilistic models to goal recognition assume that the recognizer has access to a prior probability representing, for example, an agent's preferences, virtually no goal recognition approach actually uses the prior in practice, simply assuming a uniform prior. In this paper, we provide a model to both extend landmark-based goal recognition with a probabilistic interpretation and allow the estimation of such prior probability and its usage to compute posterior probabilities after repeated interactions of observed agents. We empirically show that our model can not only recognize goals effectively but also successfully infer the correct prior probability distribution representing an agent's preferences.

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推断代理偏好作为概率目标识别的先验

目标识别的最新方法已利用规划界标来以较低的运行时成本实现高精度。但是，这些方法缺乏概率解释。此外，尽管大多数目标识别的概率模型都假定识别器可以访问表示例如代理人偏好的先验概率，但实际上，实际上没有目标识别方法实际上在简单地假设统一先验的情况下使用先验。在本文中，我们提供了一个模型，该模型既可以扩展具有概率解释的基于地标的目标识别，也可以估计这种先验概率，并将其用于在观察到的代理人反复互动后计算后验概率。