Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
A Framework for Developing Mobile, Context-aware Applications
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Understanding context before using it
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
A reasoning framework for ambient intelligence
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Context Inference Engine (CiE): Inferring Context
International Journal of Advanced Pervasive and Ubiquitous Computing
| Hi-index | 0.00 |
Context aware computing is a computational paradigm that has faced a rapid growth in the last few years, especially in the field of mobile devices. One of the promises of context-awareness in this field is the possibility of automatically adapting the functioning mode of mobile devices to the environment and the current situation the user is in, with the aim of improving both their efficiency (using the scarce resources in a more efficient way) and effectiveness (providing better services to the user). We propose a novel approach for providing a basic infrastructure for context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks, and ontologies) are applied to context inference. The aim is to devise a general inferential framework to easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts. In previous contextaware applications, most researches focused almost exclusively on time and/or location and other few data, while the same contexts inference was limited to preconceived values. Our approach differs from previous works since we do not focus on particular contextual values, but rather we have developed an architecture where managed contexts can be easily replaced by new contexts, depending on the different needs. Moreover, the inferential infrastructure we designed is able to work in a more general way and can be easily adapted to different models of applications distribution. We show some concrete examples of applications built upon the inferential infrastructure and we discuss its strengths and limitations.