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Revue d'Intelligence Artificielle

Revue des Sciences et Technologies de l'Information
Nouvel éditeur en 2019

 ARTICLE VOL 22/5 - 2008  - pp.527-530

Context sensitive processing plays a key role in many modern IT applications, with context-awareness and context-based reasoning essential not only for mobile and ubiquitous computing, but also for a wide range of other areas such as collaborative software, web engineering, personal digital assistants, information sharing, health care workflow and patient control, adaptive games, and e-Learning solutions. From an intelligent systems perspective, one of the challenges is to integrate context with other types of knowledge as an additional major source for reasoning, decision-making, and adaptation and to form a coherent and versatile architecture. There is a common understanding that achieving desired behaviour from intelligent systems will depend on the ability to represent and manipulate information about a rich range of contextual factors. These factors may include not only physical characteristics of the task environment, but many other aspects such as the knowledge states (of both the application and user), emotions, etc. This representation and reasoning problems present research challenges to which methodologies derived amongst others from artificial intelligence, knowledge management, human-computer interaction, and psychology can contribute solutions. The papers published in this issue represent a selection of the best papers from the CONTEXT-07 Workshop “Modeling and Reasoning in Context” (MRC 2007). Each paper has been selected based on their ranking in the initial review for the workshop and by popular vote through a survey made amongst the participants of the workshop. Each paper has been extended and accepted in a second reviewing process. They all have been enriched by taking into account seven dimensions of context that appear recurrently, namely: 1) Domain covered, 2) topics addressed, 3) theoretical framework described, 4) formalism used, 5) type of application (realworld, case study, toy example), 6) target of the work (a system, a user, a concept), and 7) the view on context. In “Representation of contextualized (prescribe) tasks”, Brézillon and Brézillon dig the well-known distinction between what is planned and what is done. One contrasts the prescribed task to the effective task, the logic of functioning to the logic of use, procedures to practices, etc. However, there is no real analysis of the differences because of the large variety of effective tasks for a given prescribed task. Indeed, the authors show that there is a solution, thanks to a uniform representation of elements of reasoning and of contexts, called Contextual Graphs. They propose a notion of contextualized task model that is an operational intermediate between prescribed and effective tasks.




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