The easy creation of context-aware services requires the support of management facilities that provide ways to more easily acquire, represent and distribute context information. This paper claims that the quality level of a context-aware service determines the context information to be obtained. On the other hand, using context data produced by unsteady sources may affect the users’ satisfaction. In this perspective, we introduce the Context Matching Engine that trades off the cost, the user preferences and the quality of the available context information in order to discover the best context sources for each customized context-aware service. According to the proposed approach, there is no need for the services to know beforehand the context providers to retrieve information, but the evaluation and the quality-aware selection of the context information on context request are envisioned. Finally, it allows services to be ported easily to environments with different set of context sources.
One of the cornerstones of any intelligent entity is the ability to understand how occurrences in the surrounding world influence its own behaviour. Different states, or situations, in its environment should be taken into account when reasoning or acting. When dealing with different situations, context is the key element used to infer possible actions and information needs. The activities of the perceiving agent and other entities are arguably one of the most important features of a situation; this is equally true whether the agent is artificial or not. This work proposes the use of Activity Theory to first model context and further on populate the model for assessing situations in a pervasive computing environment. Through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in our ambient intelligent system, is designed.
In the research project EPOS we build a pro-active, context-sensitive support system to aid the user with his knowledge work, which is mostly about searching, reading, creating, and archiving of documents. In order to avoid distracting the user, the context gathering is realized by installable user observation plugins for standard applications such as Mozilla Firefox and Thunderbird. The main part of this paper is about the definition of a context model for the personal knowledge management domain. The context model incorporates only contextual elements relevant to satisfy the knowledge worker's potential information need. It stores only information items known to the user (such as links to his own documents, folders, etc.), as well as, shared ontologies to assure an understanding of the context. The context is modeled in RDF/S and can be retrieved by contextaware applications from the context support system via an XML-RPC call.
Applications need facilities for recognizing and adapting to context in order to provide useful and user-centered results. There are several problems to be addressed when building context-aware applications, two of which being how to define and manage all available contextual information and how to distinguish relevant from non-relevant context for a given task. In this paper, we focus on the second problem and propose a context definition and model for a context-aware agent. We exploit this model to build agents that learn to select relevant context and to use it to make decisions.
