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Using Ontology to Drive an Adaptive Learning Interface

Author(s): Andrew Crapo | Amy Aragones | Joseph Price | Anil Varma

Journal: Journal of Systemics, Cybernetics and Informatics
ISSN 1690-4532

Volume: 2;
Issue: 5;
Start page: 44;
Date: 2004;
Original page

Keywords: ontology | autonomic logistics | Machine Learning | conceptual graphs | adaptive interface

Intelligent, adaptive interfaces are a pre-requisite to elevating computer-based applications to the realm of collaborative decision support in complex, relatively open-ended domains such as logistics and planning. This is because the composition and effective presentation of even the most useful information must be tailored to constantly changing circumstances. Our objective is to not only achieve an adaptive human-machine interface, but to imbue the software with a significant portion of the responsibility for effectively controlling the adaptation, freeing the user from unnecessary distraction and making the human-machine relationship more collaborative in nature. The foundational concepts of interface adaptation are discussed and a specific logistics application is described as an example.
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