An Ambient Intelligent Prototype for Collaboration

An Ambient Intelligent Prototype for Collaboration

Violeta Damjanovic (Salzburg Research, Austria)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-000-4.ch005
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In this article, we explore the impact of ambient intelligence (AmI) on collaborative learning and experimental environments aiming to point out some new and upcoming trends in the professional collaboration on the Web. The article starts with some introductory explanations of both Web-based and ubiquitous environments. In addition, an overview of the relevant research issues is given. These issues represent the key paradigms on which the conceptual design of the AmIART prototype is based, and embrace the following facets: Ambient Intelligence, online experimenting, and personalized adaptation. The main idea of the AmIART prototype is to give users the feeling of being in training laboratories and working with real objects (paintings, artifacts, experimental components). Then, the AmIART prototype for fine art online experimenting is discussed in the sense of e-collaboration. When online experiments are executed in the Semantic Web environment, remote control of experimental instruments is based on knowledge that comes from domain ontologies and process ontologies (semantic-based knowledge systems). For these purposes, we present the ontology ACCADEMI@VINCIANA, as an example of a domain ontology (professional training domain), as well as the ontology GUMO (general user model and context ontology) that consists of a number of classes, predicates and instances aimed at covering all situational states and models of users, systems/devices and environments. In the following section, a collaborative scenario of using the AmiART prototype is given. The last section contains some conclusion remarks.

Key Terms in this Chapter

Online Experiments: Online experiments present such kind of experiments that enable users to get an experience without leaving their workplace and going to a traditional laboratory. They are based on remotely controlling experiment equipment or software simulations of real experiments built for learning purposes.

Semantic Web: The Semantic Web is a vision of an extension of the current web in which data are given meaning through the use of a series of technologies.

Ontology: An ontology is a formal description of the meaning of the information stored in a system. Ontology provides explicit domain theories that can be used to make semantics of information explicit and machine processable.

Personalized Adaptation: Personalized adaptation is a key aspect of advanced, technology enhanced learning environments that supports ubiquitous, decentralized, agent-based systems and devices for learning, training, and generally performing well in different environments.

Context Management: The goal of context management part is to design and implement a mechanism by which context information can be updated and distributed.

Ambient Intelligence: Ambient intelligence represents the vision of a world in which technology is integrated into almost everything around us. It is provided through interaction and/or participation and can be appreciated more as an assistive feature of the system, which addresses the real needs and desires of the user.

E-Collaboration: E-collaboration represents the convergence of technologies to allow people to work together.

Ubiquitous Computing: The idea of ubiquitous computing as invisible computation was first articulated by Mark Weiser in 1988 at the Computer Science Lab at Xerox PARC. It can be defined as integration of microprocessors into everyday objects like furniture, clothing, toys, roads, smart materials.

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