An important feature of pervasive, intelligent assistance systems is the
ability to dynamically adapt to the current needs of their users. Hence, it is
critical for such systems to be able to recognize those goals and needs based
on observations of the user's actions and state of the environment. In this
work, we investigate the application of two state-of-the-art, planning-based
plan recognition approaches in a real-world setting. So far, these approaches
were only evaluated in artificial settings in combination with agents that act
perfectly rational. We show that such approaches have difficulties when used to
recognize the goals of human subjects, because human behaviour is typically not
perfectly rational. To overcome this issue, we propose an extension to the
existing approaches through a classification-based method trained on observed
behaviour data. We empirically show that the proposed extension not only
outperforms the purely planning-based- and purely data-driven goal recognition
methods but is also able to recognize the correct goal more reliably,
especially when only a small number of observations were seen. This
substantially improves the usefulness of hybrid goal recognition approaches for
intelligent assistance systems, as recognizing a goal early opens much more
possibilities for supportive reactions of the system.

本文研究了两种先进的基于计划的计划识别方法在现实环境中的应用。我们发现这些方法在识别人类行为目标时存在困难，因为人类行为通常不是完全理性的。为了克服这个问题，我们提出了一种通过分类方法进行扩展的方法，该方法通过训练观察行为数据，不仅优于完全基于计划的和纯数据驱动的目标识别方法，而且能够更可靠地识别正确目标，特别是当只看到少量观察时，这大大提高了混合目标识别方法在智能辅助系统中的实用性。