An adaptive rule-based approach for managing situation-awareness

This framework adopts Semantic Web reasoning, fuzzy logic modeling, and genetic algorithms to handle, respectively, situational/contextual inference, uncertain input processing, and adaptation to the user's behavior to manage situation awareness.

Abstract

Situation awareness is a powerful paradigm that can efficiently exploit the increasing capabilities of handheld devices, such as smart phones and PDAs. Indeed, accurate understanding of the current situation can allow the device to proactively provide information and propose services to users in mobility. Of course, to recognize the situation is a challenging task, due to such factors as the variety of possible situations, uncertain and imprecise data, and different user's preferences and behavior. In this framework, we propose a robust and general rule-based approach to manage situation awareness. We adopt Semantic Web reasoning, fuzzy logic modeling, and genetic algorithms to handle, respectively, situational/contextual inference, uncertain input processing, and adaptation to the user's behavior. We exploit an agent-oriented architecture so as to provide both functional and structural interoperability in an open environment. The system is evaluated by means of a real-world case study concerning resource recommendation. Experimental results show the effectiveness of the proposed approach.