Abstract
The proliferation of wireless technologies combined with the “anywhere and at any time” paradigm poses new scenarios for content provisioning. These scenarios demand appropriate technological support capable of dynamically adapting different contents according to the context under analysis, which depends on the user preferences, the device features, the network specification, the contract information, and the content description. Therefore, mobile applications must be able to dynamically manage different profiles by considering specific issues, such as: user satisfaction, distributed environments, heterogeneous devices and content personalization. Contributing to this field, the authors propose an approach centered on intentional Multi-Agent Systems to deal with the dynamic content adaptation in mobile applications. A dynamic database based on a meta-architecture supports the storage and the retrieving of the profiles’ information. Moreover, the authors developed a framework, which is provided as an API to promote the reuse of our content adaptation approach in different mobile projects. In this chapter, they describe their proposal and present its application to a case study from the dental clinic domain. Moreover, they evaluate the dental clinic application and the experience acquired is also reported on this work. Finally, the authors compare their approach with some related work by emphasizing its benefits and drawbacks.
TopTechnological Support
As follows, we briefly describe the conceptual foundation as well as the technological set applied to our approach, based on the investigation of the Computer Science literature.
Key Terms in this Chapter
Goal-Oriented Requirements Engineering (GORE): Requirements Engineering focused on activities that precede the requirements specification, by obtaining models that can be used during the design stage to drive and validate architectural decisions.
Intentional Multi-Agent Systems (Intentional MASs): Autonomous Entities Organization, whose cognitive capacity of its members is improved by the intentionality concept. Here, the intentionality is the property of being about or directed toward a subject.
Belief-Desire-Intention Model (BDI Model): Model centered on belief, desire and intention abstractions, normally applied to the implementation of intentional agents. The belief represents the agent’s knowledge, which can be based on information of the real world. The desire is the agent’s goal. The intention is a sequence of tasks to achieve the specified goal.
Distributed Intentionality Modeling (i* Modeling): Modeling based on the specification of goals, softgoals, tasks, resources and beliefs to capture stakeholders’ purposes, which are related to functional and non-functional requirements. It is adequate to understanding social and organizational contexts.
Dynamic Content Adaptation: Content adaptation performed “on the fly” by taking into account the context under analysis to better provide personalized contents. It may demand a dynamic database, which is generally designed based on a meta-architecture to allow the storage and retrieving of data at runtime.