Adaptive Context Exploitation

摘要

This paper presents concepts and an implementation scheme to improve information exploitation processes and products by adaptive discovery and processing of contextual information. Context is used in data fusion - and in inferencing in general - to provide expectations and to constrain processing. It also is used to infer or refine desired information ("problem variables") on the basis of other available information ("context variables"). Contextual exploitation becomes critical in several classes of inferencing problems in which traditional information sources do not provide sufficient resolution between entity states or when such states are poorly or incompletely modeled. An adaptive evidence-accrual inference method - adapted from developments in target recognition and scene understanding - 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 Joint Directors of Laboratories (JDL) Data Fusion Model, with its extension to dual Resource Management functions, has been adapted to accommodate adaptive information exploitation, to include adaptive context exploitation. The interplay of Data Fusion and Resource Management (DF&RM) functionality in exploiting contextual information is illustrated in terms of the dual-node DF&RM architecture. An important advance is in the integration of data mining methods for data search/discovery and for abductive model refinement.