Abstract-When we connect smart devices to one another we open up many new possibilities. One interesting possibility is to support high-level semantic interaction without requiring multiple steps on multiple devices. In this paper we investigate how ontologies, runtime task models, Belief-Desire-Intention (BDI) models, and the blackboard architectural pattern may be used to enable semantic interaction for pervasive computing. An initial demonstrator was developed to visualize and manipulate semantic connections between devices in a smart home environment. The demonstrator provides a way for users to physically interact with devices on a high level of semantic abstraction without being bothered with the low-level details.
Abstract-In envisioned smart environments, enabled by ubiquitous computing technologies, electronic objects will be able to interconnect and interoperate. How will users of such smart environments make sense of the connections that are made and the information that is exchanged? This Internet of Things could have a life of its own, exchanging digital concepts and values between its members, having an understanding of each other and communicating in their own language. In this paper we report on an ongoing research project in the context of smart home environments. We discuss possibilities to represent this digital world in the physical reality we live in, by providing handles to control and clues to understand, build conceptual models of connections that exist or can be made. This is achieved by making semantic abstractions of low-level events and presenting them to users at a higher level, in a simplified fashion. Furthermore, we used an ontology to describe the low-level events, and used reasoning to infer high-level meaningful information. Although we are in the preliminary stages of our research, we consider it worthwhile to share and illustrate our findings by presenting a demonstrator, that implements our ideas in a home entertainment scenario.
Abstract. Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We successfully used the Embodied Intelligence method to teach machine learning to our students. By embodying the learning system into the Lego Mindstorm NXT platform we provide the student with a tangible tool to understand and interact with a learning system. The resulting behavior of the tangible machines in combination with the positive associations with the Lego system motivated all the students. The students with less technology affinity successfully completed the course, while the students with more technology affinity excelled towards solving advanced problems. We believe that our experiences may inform and guide other teachers that intend to teach machine learning, or other computer science related topics, to design students.
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