Context is used in data fusion to provide expectations and to constrain processing. It also is used to infer or refine inferences of desired information (“problem variables”) on the basis of other available information (“context variables”). Context is used in refining data alignment and association as well as in target and situation state estimation. Relevant contexts are often not self-evident, but must be discovered or selected as a means to problem-solving. Therefore, context exploitation involves an integration of data fusion with planning and control functions. Discovering and selecting useful context variables is an abductive data fusion/ management problem that can be characterized in a utility/uncertainty framework. An adaptive evidence-accrual inference method – originally developed for Scene Understanding – is presented, whereby context variables are selected on the basis of (a) their utility in refining explicit problem variables (expressed as mutual information), (b) the probability of evaluating these variables to within a given accuracy, given candidate system actions (data collection, mining or processing), and (c) the cost of such actions.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com