Adaptive context discovery and exploitation

Context is used in data fusion - and in decision-making in general - to establish expectations, to resolve ambiguity and assess performance. We model this in terms of “context variables” which a (human or automated) decision system seeks, discovers and evaluates as a means to infer or refine desired information (“problem variables”). An adaptive evidence-accrual inference method is presented, whereby context variables are selected on the basis of (a) their utility in refining explicit problem variables, (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. The JDL Data Fusion Model, extended to dual Resource Management functions, has been refined to accommodate adaptive decision, to include adaptive context exploitation. The interplay of Data Fusion and Resource Management (DF&RM) functionality in exploiting contextual information is developed by means of the DF&RM technical architecture. An important advance is in the integration of data mining methods for data search/discovery and for inductive and abductive model refinement.