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Best Paper

By Nitya
Update: Sorry about that -- didn't realize this got posted initially with no content. As Steve pointed out in comments, the votes were close. After tallying the online voting with in-attendance votes (from those who didn't make the online poll before it closed), Todor made a strong comeback but Ian Wakeman et. al. finally triumphed with a 9-7 edge. Congratulations to them, and we look forward to seeing you all again next year.
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Posting abstracts for papers ..

By Nitya
The workshop is just over ten days away. In preparation for the discussions, and also to get the attendees more conversant with the researchers and papers to be presented, we will be posting the abstracts for accepted papers (one per blog item) over the next few days. If you are the author of a paper, and want to add links in to related research or a personal website for more information, please feel free to do so. For convenience, the blog posting will be prefixed with a number of the form S.P, where S is the session number (1, 2 or 3) and P is the paper number within that session. Each session will conclude with a panel focussed on discussion around the papers presented. In that context, if you have specific questions for authors, or have comments on the topic -- feel free to post them against the relevant blog items.. Update: I'm just waiting on some clarifications from ACM before I release the abstracts... do check back. Update: ACM granted us permissions -- the abstracts will...
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2.2 A Probabilistic Reasoning Framework for Smart Homes

By Nitya
Todor Dimitrov, Fraunhofer IMS Josef Pauli, Universität Duisburg-Essen Edwin Naroska , Fraunhofer IMS Abstract: Inference and reasoning in modern AmI (Ambient Intelligence) middlewares is still a complex task. Currently no common patterns for building smart applications can be identified. This paper presents an ongoing effort to build a generic probabilistic reasoning framework for the networked homes. The framework can be utilized for designing smart agents in a systematic and unified way. The developed modeling and reasoning algorithms make an extensive use of the information about the user and the way he/she interacts with the system. To achieve this, several levels of knowledge representation are combined. Each level enriches the domain knowledge in a way that a consistent, user-adaptable probabilistic knowledge base is constructed. The facts in the knowledge base can be used to encode the logic for a specific application scenario. ACM Copyright Notice Copyright © by the Associat...
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