A Human-Machine Dimensional Inference Ontology that Weaves Human Intentions and Requirements of Context Awareness Systems

Abstract

Changing system requirements, especially for context awareness (CA) systems, often cause modifications in the software systems in order to adapt to dynamic environments. Since the requirements may become temporarily obsolete or contrary to human intentions, the CA systems need to be tuned to resolve the conflict. On the other hand, most CA design methods rely on pre-defined requirements and reasoning engine, thus, fail to address all the possible situations. Consequently, services provided by such a CA system are limited to accommodate some situations and unable to react as expected. Therefore, it is critical for CA systems to capture exceptions at runtime, infer changed human intentions, and adapt to these changes. This study focuses on inference of ever-changing human intentions and monitoring human intentions to handle system evolution. In this paper, we present an inference mechanism of human intentions via the human-machine dimensional inference ontology (HDIO). This ontology gives inference rules based on the BDI logic to deduce human intentions from contexts. Furthermore, the inference exercises of a healthcare system example shows how user intentions relate to system requirements and how they help improve self-adaptability of CA systems.