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Please join the conversation ..

We are trying out something new this year at MPAC -- a blog!

We see this as an opportunity to start a conversation with workshop participants (and the general research community) in advance, and then continue to stay connected with those researchers and their progress after the event. We plan to use this forum to post questions (surveys) to get your feedback -- and also to help you (as researchers) publicize your work or start discussions around topics of interest to the wider community.

If you are interested in being a contributor, email us (mpac dot pc at gmail dot com) and we'll add you to our authors list.

Anyone can post comments though -- so do share your opinions and comments freely. This forum is meant for the research and user communities, so please do use appropriate language.

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Best Paper

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.

Posting abstracts for papers ..

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2.2 A Probabilistic Reasoning Framework for Smart Homes

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...