WAmIi 2013 - The 2013 Workshop on Ambient Intelligence Infrastructures

One of the main challenges in Ambient Intelligence (AmI) is the design and the development of infrastructures that support interactive user control and intelligent behavior. A balance between these two aspects is the key to improve user experience and acceptance. Machine Learning (ML) is often proposed as a solution to provide intelligent behavior, for example in intelligent lighting solutions, while examples of its application as part of AmI infrastructures are rather limited in quantity and complexity. The workshop will facilitate an overview of results achieved and work currently done in this domain, for example in European projects. Our goal is to examine ML in AmI and its relation to the infrastructure to identify the white spaces and prepare the ground for future work.
The workshop will bring together members of the industrial and scientific communities that contribute to the development of ML in the context of Ambient Intelligence infrastructures. The contributions of the workshop participants will help to further understand the architecture of AmI learning systems. This overview will provide further insight into the problems solved at this stage, advantages and disadvantages of the various approaches used, and lessons learned.
The workshop discussions session will allow identifying the “white spaces” that have not yet been investigated at this stage, thereby preparing the ground for future meaningful contributions that draw from a pool of knowledge provided by a whole community rather than a particular team.
Topics for presentations
The workshop discussions are open to topics related to the architecture, design and implementation of AmI infrastructures that show and support intelligent learning behavior and interaction of users with these. In order to give the discussion an application focus, the workshop is intended to address these challenges in the context of intelligent lighting infrastructures. Examples include but are not limited to:
Architecture/design criteria for AmI infrastructures facilitating learning behavior,
Embedded intelligence, distributed intelligence and learning behavior,
Intelligent lighting applications, services, performance metrics
User interaction and user experience with intelligent lighting applications
Resource management and Quality of Service (QoS) management