A blackboard architecture for countering terrorism

This paper addresses the problem of detecting and countering potential terrorist threats. It describes a synergistic conjunction of intelligent technologies with a view towards that goal. Many databases exist, which when fused through the use of intelligent agents can provide an intermediary database that often contains critical information. The goal of this paper is to show how a directed mining approach using a blackboard architecture can be designed to extract the maximum amount of relevant information from the database, while filtering out the irrelevant information in tractable timeframes. Blackboard architectures are rule-based and allow for the incremental insertion of knowledge. Subsumption and contradiction can be automatically detected and remedied. Moreover, the use of a segmented knowledge base allows computational resources to be focused where they will do the most good. Objects for directed mining and linking are invoked as rule consequents in a blackboard architecture. The blackboard architecture provides for chaining and incremental knowledge acquisition (including maintenance). Using information hiding, objects can be visually displayed in the form of a tree to facilitate acquisition and maintenance operations. Predicate knowledge is represented using a VHLL that provides several attendant benefits. First, higher-level languages are not only easier to use, but they tend to be self-documenting. Second, the higher the level of language, the more reusable are components written in the language. Not only does this save on the cost of generating new components, but it also serves to further their testing (i.e., debugging).