The vision of pervasive computing is floating into the domain of the household and aims to assist inhabitants (users) to live more conveniently and harmoniously. Due to the dynamic and heterogeneous nature of pervasive computing environments, it is difficult for an average user to obtain right service and information in the right place at the right time. This chapter proposes a context-dependent task approach to address the challenge. The most important component is its task model, which provides an adequate high-level description of user-oriented tasks and their related contexts. Leveraging the model, multiple entities can easily exchange, share, and reuse their knowledge. The conversion of OWL task ontology specifications to the First-Order Logic (FOL) representations is presented. The performance of FOL rule-based deducing in terms of task number, context size, and time is evaluated. Finally, we present a task supporting system (TSS) to aid an inhabitant’s tasks in light of his or her lifestyle and environment conditions in smart home.
High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons
