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People can usually sense troubles in a car from noises, vibrations, or smells. An experienced driver can even tell where the problem is. We call this kind of skill .

Ambient Diagnostics is an emerging field that is aimed at detecting abnormities from seemly disconnected ambient data that we take for granted. For example, the human body is a rich ambient data source: temperature, pulses, gestures, sound, forces, moisture, et al. Also, many electronic devices provide pervasive ambient data streams, such as mobile phones, surveillance cameras, satellite images, personal data assistants, wireless networks and so on.

The ability to determine the location of a mobile device is a challenge that has persistently evaded technologists. Although solutions to this problem have been extensively developed, none provide the accuracy, range, or cost-effectiveness to serve as a solution over a large urban area. The Global Positioning System (GPS) does not work well indoors or in urban environments. Infrared based systems require line-of-site, are costly to install and do not perform well in direct sunlight [1]. Cellular network-based positioning systems are limited by cell size and also do not work well indoors [23]. The list goes on. With the rise of Wireless Internet, or WiFi as it is commonly dubbed, the best infrastructure for location awareness to date has been created. WiFi is standardized, inexpensive to deploy, easy to install and a default component in a wide-range of consumer devices. These characteristics are the drivers behind WiFi’s most significant trait: increasing ubiquity. By developing within the existing 802.11 infrastructure, developers can leverage WiFi to create wide-spread context-aware services.

Ambience provides large amounts of heterogeneous data that can be used for diverse purposes, including indoor navigation in semi-structured environments. Indoor navigation is a very active research field due to its large number of possible applications: mobile guides for museums or other public buildings [36], office post delivering, assistance to people with disabilities and elderly people [34], etc.

The idea of using indoor navigation techniques to develop mobile guides is not new. Among the pioneers, Polly, a mobile robot acting as a guide for the MIT AI Lab [35], and Minerva, an autonomous guide developed for the National Museum of American History in Washington [69], are well known. A particular case are mobile guides for blind people which experienced a notable interest in the last years [40]. Another interesting application field is devoted to smart wheelchairs, which are provided with navigation aids for people with severe motor restrictions [64,75]. All these applications share the need for a navigation system, even if its implementation may be different for each of them. For instance, the navigation system may act over the power stage of a smart wheelchair or may communicate with the user interface of a mobile navigation assistant in a museum. Evidently the implication of the user is different in each system, leading to diverse levels of human-system integration. Therefore, there are two key issues in the design of mobile guides: navigation strategy and user interface. Even if most of the mentioned systems use maps for navigation [36], there exist alternative, behavior-based systems, that use a procedural way to represent knowledge. Therefore, the selection of the approach not only conditions the navigational architecture but also the design of the human interface.

This chapter analyzes alternatives for navigation models and focuses on how properties of the environment can be intelligently exploited for indoor navigation tasks. In addition, it describes, in detail, an illustrative example based on behavior decomposition. Its navigational characteristics and influence upon the human interface design are also discussed.

The vision of ambient intelligence is one where the populas is supported in the conductance of their everyday lives through the pro-active, opportunistic support of non-intrusive computing devices offering intuitive interaction modalities.

This chapter advocates the adoption of mobile intentional agents as a key enabler in the delivery of ambient intelligence. Ambient computing, as an ideal, demands levels of functional attainment that have hithertofar not been realized. Ambient applications demand that the computing application be subsumed into the everyday context in an unobtrusive manner with interaction modalities which are natural, simple and appropriate to both the individual user and their associated context.