Complex, dynamic problems in general present a challenge for the design of analysis support systems and tools
largely because there is limited reliable a priori procedural knowledge descriptive of the dynamic processes in the
environment. Problem domains that are non-cooperative or adversarial impute added difficulties involving
suboptimal observational data and/or data containing the effects of deception or covertness. The fundamental nature
of analysis in these environments is based on composite approaches involving mining or foraging over the evidence,
discovery and learning processes, and the synthesis of fragmented hypotheses; together, these can be labeled as
sensemaking procedures. This paper reviews and analyzes the features, benefits, and limitations of a variety of
automated techniques that offer possible support to sensemaking processes in these problem domains.