Agent-Oriented Context-Aware Platforms
Supporting Communities of Practice in Health Care
Luiz Olavo Bonino da
University of Trento
Via Sommerive, 14
38100 Povo, TN, Italia
Renata S. S. Guizzardi
University of Twente
P.O. Box 1212
Enschede, the Netherlands
Marten van Sinderen
University of Twente
P.O. Box 1212
Enschede, the Netherlands
This paper presents and discusses the use of an agent-oriented
context-aware platform to support communities of practice (CoPs)
in the health care domain. Our work is based on a scenario where
CoPs are applied in a hospital to enhance the knowledge sharing
among the staff members who share interests and goals. Here, we
test the support of an agent-oriented modeling language
(AORML) for the analysis of the proposed application for the test
Categories and Subject Descriptors
H.4 [Information Systems Applications]: Miscellaneous
Agent-Oriented Languages, CoPs, Context-Aware Platforms
Context-Aware computing allows software applications to use
information beyond those directly provided as user input.
Therefore, identical sets of user input data can produce different
outputs since contextual information is considered in the
processing, and context can change at each system invocation. In
addition to that, this facilitates the use of these applications by
minimizing user intervention, and increases their independency in
gathering and processing relevant information. Here, a context is
depicted by any relevant information that can be used to
characterize the situation of an entity.
These features allow us to build more flexible systems to support
knowledge sharing in communities of practice (CoPs) , which
are dynamic environments, composed of people sharing interests
and goals. Having this in mind, we propose the use the Context-
Aware Services Platform (CASP ) to support CoPs in the
health care domain.
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CASP provides an infrastructure supports the use of semantically
annotated web services, and it offers dynamic service discovery,
dynamic deployment of new services, and context gathering from
third party context providers or sensors. Agents have been used as
an enabling technology for organizational settings [1,2]. In our
perspective, the agent paradigm is suitable i) regarding CoPs, by
providing a suitable metaphor used for modeling KM domains
and systems; and ii) concerning context-aware applications,
agents seem to be a natural technological approach to model and
develop them, having context providers viewed as agents’ sensors
or other agents of the environment. But although agents seem to
comply perfectly with the requirements of developing such
applications, having the right abstraction is not enough for
guaranteeing the development of adequate solutions. For that,
consistent software engineering methods and languages are
needed. In this particular paper, we test the usability of the Agent-
Object-Relationship modeling language (AORML) .
In section 2, we present our proposed scenario; section 3
exemplifies the scenario modeling with the use of AORML; and
section 4 concludes this paper.
2. HEALTH CARE SCENARIO
The ABC hospital sponsors CoPs development across its units.
Although these communities are self-organizing groups, the
hospital management fostered their formation, by providing
support to their initial configuration. This has been achieved with
the following measures: i) in the initial configuration, the CoPs’
organization reflected the division of medical specialties; then ii)
a context-aware application named Interact was deployed,
supporting the emergence of new CoPs, based on interactions
between members of each unit. Interact is a Client of the CASP
platform, and uses an existing service named FindAFriend (FF) to
find some collaborators among the hospital staff that may share
interests with him/her. Interact allows the CoPs’ members to fill
in their profiles and interact with each other by sending e-mails,
submitting comments to newsgroups, and using instant messaging.
The members’ profiles and the information embedded on their
interactions are considered as contextual information. This
information is analyzed by FF, which identifies related interests,
cognitive and social characteristics for creating new CoPs.
3. SCENARIO MODELING WITH AORML
Figure 1 presents an AOR agent diagram. Here follows a
description of each agent in the scenario:
• Hospital ABC: organization where the CoPs are developed.
• CoP: communities of practice created within and across the
units of Hospital ABC.
• Management: fosters CoPs within Hospital ABC.
• Member: the CoPs’ participants and the actual users of the
context-aware system. Within this class, the object Medical
Specialty is the basis for the creation of the first CoPs.
• CASP Platform: context-aware services platform.
• Client: accesses services through CASP, being the actual end-
• Interact: client used by Members to collaborate and receive
suggestions about the creation of new CoPs.
• Context Provider: provides contextual information gathered by
sensors or third party Context Providers.
• AllPersonnelCP: the Context Provider of our application.
• Service Provider: offers services by registering their
description to the CASP platform.
• FindAFriend: service that uses contextual information of the
Members to propose the creation of new CoPs.
A concrete example of Interact’s use is the following: Ronald, a
cardio-vascular surgeon exchanges e-mails with Sanny, a plastic
surgeon about the implications of a particular plastic surgery
procedure in cardio-vascular condition. Although they are from
different medical specialties, thus from different initial CoPs, they
are married (social characteristic) and share common professional
interests. AllPersonnelCP submits contextual information (e.g.,
info from exchanged e-mails, data from user’s profiles) to CASP.
This triggers FindaFriend to suggest the creation of a new CoP
involving Ronald and Sanny. Figure 2 presents all messages
exchanged between Interact and the CASP Platform, and
between the CASP Platform and FindAFriend, particularly
illustrating the AORML commitment construct. Note that the
delivery of such recommendation to the users has been suppressed
here. A commitment entitled provideSrv is automatically created
between Interact and the CASP Platform when CASP
acknowledges a request for a service execution has been received
(AckReqSrv message). As depicted in Fig. 5, these two messages
form the parameters of the provideSrv commitment, connected
by an XOR diamond indicating that either of these two messages
leads to the fulfillment of this commitment. Any other result
consequently fails the established commitment. This commitment
guarantees that CASP will either provide the requested service or,
at least, send a ‘service execution failed’ message. This type of
agreement between the agents is particularly interesting in
situations like the one described here, where CASP and Interact
are systems developed by different parties.
After using the AOR modeling approach in our scenario, we
found it to be quite suitable to model context aware systems. First,
AORML presents the advantage of modeling both active and
passive entities of the scenario, differentiating between agents and
objects. We found the combination of both agents and objects to
be very useful, since in context-aware applications, not all entities
should be represented as agents, but only those that have
intentionality. The resources used by the agents, as well as agents’
beliefs may be well represented as objects. Besides, AORML
allows interaction modeling through three kinds of diagrams, and
models agent’s reactive behavior through rules.
 Dignum, V. A model for organizational interaction: based on
agents, founded in logic. PhD Thesis. Utrecht Univ., Jan. 2004.
 Guizzardi, R., Perini, A., Dignum, V. Providing Knowledge
Management Support to CoPs through Agent-oriented Analysis.
In Proc. of 4th Int. Conf. on KM., Graz, Austria, June/2004.
 Santos, L.O.B.S., Semantic Services Support for Context-
Aware Platforms, MSc Diss., UFES, Brazil, Sept., 2004.
 Wagner, G. The Agent-Object-Relationship Meta-Model:
Towards a Unified View of State and Behavior. In Information
Systems, 28:5, 2003.
Figure 1 - Scenario modeled using the AOR Agent Diagram
Figure 2 – AORML Interaction Sequence Diagram Showing
Service Request and Execution