Combining Design of Models for Smart Environments with Pattern-Based Extraction
There are two different types of approaches for smart environments. The first group provides an infrastructure that contains mechanisms from artificial intelligence that allow to adapt to certain behavior of users and to support them by performing their tasks. These approaches work fine if the conditions in the environment are not experiencing too many changes. However, when different types of activities have to be supported and participants change a lot there is the problem of getting enough training data to recognize the users’ activities with sufficient reliability. In such cases, designing support by providing models for activities of participating users seems to be a solution. Thus, mechanisms from artificial intelligence can be supported by reducing the search space for possible actions.
Designing of activity models can be performed by employing the top-down approach through predefined generic patterns or alternatively the bottom-up mechanism by looking at traces of performed activities (scenarios). Again patterns play an important role as they allow the identification of important parts of traces that lead to parts of models. The identification of such trace sections can be done almost automatically. The mapping to parts of models however, has to be done in an interactive way. Human decisions are necessary to provide good models. Different strategies can be supported by tools in order to make decisions within the models ranging from abstract levels down to the most detailed level.
This paper will provide a discussion of the outlined approach.
Keywordstask models smart environment model generation
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