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Inevitably at some stage the system makes an error and the resident has to take a corrective action. This enables the system to update its world model in order. Two research questions rise from this simple scenario: How to construct and update a world model? And how can the system differentiate between a corrective interaction and normal behavior of the resident. Our system answers these questions by utilizing context recognition and fuzzy control. Context recognition is an effective method for providing application-specific information and enabling context triggered actions [1] [2].

Unfortunately, sometimes it is not possible to set up a benchmark that involves the final users, especially when the system under test is an active research work. In this case literature shows that it is possible to An Accessible Control Application for Domotic Environments 25 simulate the troubles that a disabled person can find in using the application by forcing the normal users to operate in stressful conditions. This approach for example was adopted by Fraser and Gutwin [19] in their work where, to simulate a visually impaired person, a normal user was constrained to look at a screen from a very far point, thus inducing a situation actually near to the one of a low vision user.

The presentation of uncertainty is complicated to a computer using traditional methods. Among scientist uncertainty has been considered to be an inevitable part of speech and a precise expression has been an objective of research for a long period of time. [8] For example, for a computer it is very hard to understand what dark and bright means. Let us define a border between dark and bright to 100 luxes. If the sun shines outdoors, the illumination can be measured to be 50 000 luxes. Here it is quite easy to say that it is bright outside.

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