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
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.