Recently, there is an increased research interest in context-aware systems that are able to autonomously and intelligently support users with their tasks. An important feature of such 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 the current activities and goals of their users. While there is some work on the problem of goal recognition in this context, the majority of research works focus on the problem of recognizing a user's current activities.
Further, the existing methods mostly rely on purely symbolic methods, which have problems to handle low signals in the observed user data.
As a consequence, these approaches are not able to reliably recognize the user goals as early as it should be possible, based on the information contained in the observed data.
This significantly reduces the usefulness of the recognized goals for context-aware support systems, because it reduces the amount of time the system has to react to recognized goals.
Hence, in our research, we focus on the combination of symbolic and data-driven methods to hybrid methods for goal recognition and their application in the context of pervasive computing environments like smart homes.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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