Based on our conviction that event management and response generation are two essential aspects of systems for Ambient Intelligence, we propose here a semantic model for actions and events as a way of handling these issues. Basically, our semantic model describes what actions and events are, how they are connected, and how computational systems should think about their meaning.
This model entails an approach with which to both reason about and model context events and generate behavioral responses to those events, when necessary. The model supports this ad-hoc response generation by automatically composing services when those which are available do not meet the expected functionality (without requesting user intervention).
The great potential of the proposed semantic model is founded on its fulfillment of the inter-module connectivity and communication requirements of a framework for Ambient Intelligence. In our endeavor to show the impact that automatic service composition has on the achievement of autonomous, self-managed, and pro-active systems, we propose an optional implementation that makes use of several technologies: ZeroC ICE, JADEX, and Scone. The only purpose of this optional implementation is showing the feasibility of our proposal and to show the reader how to map the proposed theorethical semantic model into a concrete system.
Our main contribution is twofold with regard to the semantic model. First, we propose a non-coupled semantic model. Second, as mentioned in the paper, we are convinced that the notion of event, action or service should not vary among systems, nor should they respond to the mere approach convenience. For that reason, we follow a novel approach proposing a semantic model for Ambient Intelligence based on the conclusions, concerning actions and events, drawn from the philosophical doctrine. In contrast to what can be found in the literature revision, our model is formally based and justified on philosophical studies.
From a computing perspective, a semantic model is considered to be an agreement on how to interpret the knowledge represented in the knowledge base. Semantic models therefore ensure common interpretations of shared knowledge. They are also an essential requirement when there are different instances handling the same knowledge. Every holder is expected to extract the same meaning or conclusions from the represented knowledge.
Having said that, the following section provides a more formal expression of the proposed semantic model, along with the justification of why the proposed semantic model is considered to be common-sense compliant.
The following subsections analyze those modules, paying particular attention to the implications of the semantic model and the different strategies followed for its implementation.
Furthermore, the following sequence diagrams, provide an overview of the interaction among the different modules.
The Multi-Agent System (MAS) works as a link between the Ambient Intelligence environment and the other elements of which the Ambient Intelligence framework is composed. The MAS is basically in charge of adopting the plan as outlined by the planning algorithm, and undertaking it. The interaction with the other architectural elements is, once again, supported in the semantic model. At the MAS level, the semantic model is implemented by means of an OWL ontology. The Agent Communication Language (ACL) messages, exchanged among agents, therefore contain classes of the ontology, which are simultaneously concepts of the semantic model.
We have used Jadex, a BDI Agent System that allows to develop goal oriented agents following the BDI model.