=Paper=
{{Paper
|id=Vol-550/paper-6
|storemode=property
|title=Who, Where, What, and When: Towards Characterizing Contexts in Pervasive Environments
|pdfUrl=https://ceur-ws.org/Vol-550/paper4.pdf
|volume=Vol-550
}}
==Who, Where, What, and When: Towards Characterizing Contexts in Pervasive Environments==
https://ceur-ws.org/Vol-550/paper4.pdf
Who, Where, What, and When:
Towards Characterizing Contexts in Pervasive
Environments
Alessandra Mileo
Department of Informatics, Systems and Communication, University of
Milan-Bicocca, viale Sarca 336/14, I–20126 Milan
alessandra.mileo@nomadis.unimib.it
Introduction. A key challenge in pervasive systems is that of identifying and de-
veloping appropriate formal approaches for understanding and reasoning about
contexts. This is a difficult task since there is no common agreement on what
constitutes a context and what its properties are. Although the literature con-
tains a rich variety of context models [1, 2], most of the resulting implementations
are domain-dependent and lack powerful reasoning capacities. Preliminary work
on logic-based reasoning about contexts has been proposed in [3, 4], using bridge
rules and argumentation theory. However, application-wise there are still open
questions on how to model context-related pieces of knowledge in general do-
mains, so that appropriate reasoning techniques can be applied. In order to put
the basis for a comprehensive theory of contexts one should take into account
the general structure of knowledge needed to represent contexts, in particu-
lar, what is intended as a context in pervasive environments and which compo-
nents/properties should be identified and described. System properties that are
of interest for the specification of contexts should be also considered. Motivated
by application challenges, this short paper highlights observations that can be
used to address these issues.
Generalization of Context in Pervasive Environments. Previous investigations
of context models in pervasive environments revealed that within contexts cer-
tain entities are more important than others in practice. These are location,
identity, activity and time [5, 6]. In pervasive environments, the more intuitively
relevant aspects of the context in which a person acts are who she is, which re-
sources/entities are related to her, where she is and when. The representation of
knowledge about contexts is very similar to the structure of a sentence in natural
language, where actors are the subjects and the objects, verbs are relations and
sensor data are the adjectives in that they assign a value to attributes of the
actors. As an example, in a home monitoring scenario, a generalization of actors
may correspond to the following entities: Person to be monitored, Room and
Area for localization and Object for resources. If attributes correspond to sensor
data, relations that we consider interesting depend on the task we want to solve
(localization of a person, reaction from the system, identification of an action,
etc.). The fact that a relation holds or not should be the result of reasoning
(commonsense, default, temporal) on the knowledge we want to characterize.
To enhance generality and modularity of the representation, we suggest to
represent entities at a primary level while all the other pieces of sensed infor-
mation are at a second level and can be indexed as attributes of the entities
at primary level. As for the relations between subjects, objects, and places in
pervasive environments the basic interesting relations that should be formalized
are spatial (being close/far, being in/out), temporal (occurring before/after) and
causal (occurring because of something).
Properties. In a given context, attributes and relations need to be associated
to a time stamp. In this way, the reasoning system can take into account their
dynamic evolution to characterize aspects such as commonsense, temporal rea-
soning, belief revision and update. In general the temporal evolution of a context
is one of the most important aspect to be correctly and formally characterized.
Additional properties may be needed to determine what is true in a given context
where certain objects, attributes and entities have been defined. These proper-
ties should make it possible to express belief merging and belief revision/update
in contexts.
Conclusions. We believe reasoning about contexts became harder and harder
because of the proliferation of very personal representations of context knowledge
in terms of admissible relations, their arity and their properties, which have been
developed ad-hoc for very diverse specific applications. Our attempt is that of
providing a more general and modular representation of context knowledge. The
intuition is that in this way multi-context reasoning could be supported by the
verification of formal properties at a knowledge representation level, in the same
way as human deduction is based on roles of the grammatical components of a
sentence.
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