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.
翻译:普遍、智能援助系统的一个重要特征是能够动态地适应用户目前的需求。因此,这些系统必须能够根据对用户行动和环境状况的观察,认识这些目标和需求。在这项工作中,我们调查在现实世界环境中采用两种最先进的、基于规划的计划确认方法的情况。迄今为止,这些方法仅在人工环境中与完全理性的代理人一起评价,我们表明,在使用这些方法时难以认识到人类主体的目标,因为人类行为通常不完全合理。为了克服这一问题,我们提议通过对观察到的行为数据进行基于分类的培训,扩大现有方法的范围。我们从经验上表明,拟议的扩展不仅超越了纯粹基于规划的、纯粹以数据为驱动的目标确认方法,而且能够更可靠地认识到正确的目标,特别是在只看到少量观察的情况下。这大大改进了混合目标确认方法对智能援助系统的效用,因为及早认识到系统的支持性反应的可能性大得多。