Modelling and Verification of Context-Aware Intelligent Assistive Formalism

Recent years have witnessed the expeditious evolution of intelligent smart devices and autonomous software technologies with the expanded domains of computing from workplaces to smart computing in everyday routine life activities. This trend has been rapidly advancing towards the new generation of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquire contextual information from the surrounding environment autonomously, perform reasoning on it, and then adapt their behaviors accordingly. With the proliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However, it is often challenging because the imperfect nature of context can cause the inconsistent behavior of the system. In this paper, we propose a context aware intelligent decision support formalism to assist cognitively impaired people in managing their routine life activities. For this, we present a semantic knowledge-based framework to contextualize the information from the environment using the protege ontology editor and Semantic Web Rule Language (SWRL) rules. The set of contextualized information and the set of rules acquired from the ontology can be used to model Context-aware Multi-Agent Systems (CMAS) in order to autonomously plan all activities of the users and notify users to act accordingly. To illustrate the use of the proposed formalism, we model a case study of Mild Cognitive Impaired (MCI) patients using Colored Petri Nets (CPN) to show the reasoning process on how the context aware agents collaboratively plan activities on the user's behalf and validate the correctness properties of the system.