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An Ambient Intelligent Prototype for Collaboration

An Ambient Intelligent Prototype for Collaboration

Violeta Damjanovic
Copyright: © 2008 |Pages: 7
ISBN13: 9781599040004|ISBN10: 159904000X|EISBN13: 9781599040011
DOI: 10.4018/978-1-59904-000-4.ch005
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MLA

Damjanovic, Violeta. "An Ambient Intelligent Prototype for Collaboration." Encyclopedia of E-Collaboration, edited by Ned Kock, IGI Global, 2008, pp. 29-35. https://doi.org/10.4018/978-1-59904-000-4.ch005

APA

Damjanovic, V. (2008). An Ambient Intelligent Prototype for Collaboration. In N. Kock (Ed.), Encyclopedia of E-Collaboration (pp. 29-35). IGI Global. https://doi.org/10.4018/978-1-59904-000-4.ch005

Chicago

Damjanovic, Violeta. "An Ambient Intelligent Prototype for Collaboration." In Encyclopedia of E-Collaboration, edited by Ned Kock, 29-35. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-000-4.ch005

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Abstract

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.

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