Intelligence Analysis Using Quantitative Preferences

The extended answer set semantics for simple logic programs, i.e. programs with only classical negation, allows for the defeat of rules to resolve contradictions. In addition, a partial order relation on the program’s rules can be used to deduce a preference relation on its extended answer sets. In this paper, we propose a “quantitative” preference relation that associates a weight with each rule in a program. Intuitively, these weights define the “cost” of defeating a rule. An extended answer set is preferred if it minimizes the sum of the weights of its defeated rules. We characterize the expressiveness of the resulting semantics and show how the semantics can be conveniently extended to sequences of weight preferences, without increasing the expressiveness. We illustrate an application of the approach by showing how it can elegantly express largest common subgraph and subgraph isomorphic approximation problems, a concept often used in intelligence analysis to find similarities or specific regions of interest in large graphs of observed activity.