As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
One of the goals of Ambient Assisted Living solutions is to enable elderly people to live in their established environment longer before being in need of care. To be able to support daily life sufficiently and do context reasoning, such systems need extensive amounts of context information, of which most is not immediately detectable by the non-invasive sensors commonly used in this area. Additionally, to guarantee the usability of those systems, they cannot have a static layout but have to be adaptive to the personal needs of the assisted person (AP). Hence, common Artificial Intelligence (AI) models relying on training such as Bayesian Networks, Neural Networks or Hidden Markov Models cannot easily be applied to such systems. The approach presented in this paper was developed for the SOPRANO project and solves the problem of reasoning about complex problems by distributing the AI to independent components and by using a blackboard and a light-weight ontology for communication between these components.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.