Engineering Contextual Information for Pervasive Multiagent Systems

Multiagent systems for mobile and pervasive computing should
extensively exploit contextual information both to adapt to user needs and to
enable autonomic behavior. This raises the problem of how to represent,
organize, aggregate, and make available such data so as to have it become
meaningful and usable knowledge, facilitating the design and development of
agents, and enabling them to acquire high-degrees of context awareness at
limited efforts. In this paper, we identify the key software engineering
challenges introduced by the need of accessing and exploiting huge amount of
heterogeneous contextual information. Following, we survey the relevant
proposals in the area of context-aware pervasive computing, data mining and
granular computing discussing their potentials and limitations. On these bases,
we propose the W4 model for contextual data and show how it can represent
an effective model to enable flexible general-purpose management of
contextual knowledge, to facilitate agents in achieving high degrees of
context-awareness and, overall, to facilitate the design and development of
complex multiagent systems.