A Context-sensitive Incremental Learning Paradigm of an Ubiquitous Multimodal Multimedia Computing System
In this paper, we proposed a context-sensitive incremental learning paradigm of an ubiquitous multimodal multimedia computing system. An ubiquitous computing environment supports a busy and mobile user’s need of being able to work on his task anytime and anywhere he wants. Along with user’s data (his profile, task, and application registry) the machine-acquired intelligence needs to be transported as well in order that the user could continue working on an intelligent environment. Machine intelligence is acquired through incremental learning. In a context-sensitive environment that has a rich selection of modalities and media for data input and output, an intelligent computing system could determine the I/O devices appropriate for the user’s setting after considering the user’s location, the noise level in the environment, and the presence or absence of other people in the vicinity. Every new setting (pre-condition scenario) produces a new I/O devices configuration (post-condition scenario) suited for the setting; each new scenario knowledge gets stored onto knowledge database. Overtime, the machine would have enough knowledge to deal with whatever context scenario that comes up.