Weighted Answer Sets and Applications in Intelligence Analysis
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 that it can capture negation as failure. Moreover 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 subgraph isomorphic approximation problems, a concept often used in intelligence analysis to find specific regions of interest in a large graph of observed activities.
KeywordsLogic Program Logic Programming Activity Graph Intelligence Analysis Stable Model Semantic
Unable to display preview. Download preview PDF.
- 2.Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge Press (2003)Google Scholar
- 7.Eiter, T., Faber, W., Leone, N., Pfeifer, G.: Declarative problemsolving using the dlv system. In: Logic-Based Artificial Intelligence, pp. 79–103 (2000)Google Scholar
- 8.Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the Fifth International Conference and Symposium on Logic Programming, pp. 1070–1080. MIT Press, Cambridge (1988)Google Scholar
- 9.Heuer, R.J.: Psychology of intelligence analysis. Center for the Study of Intelligence, Central Intelligence Agency (2001)Google Scholar
- 11.Syrjänen, T., Niemelä, I.: The smodels system. In: Eiter, T., Faber, W., Truszczyński, M. (eds.) LPNMR 2001. LNCS (LNAI), vol. 2173, pp. 434–438. Springer, Heidelberg (2001)Google Scholar
- 16.De Vos, M., Vermeir, D.: Logic programming agents playing games. In: Research and Development in Intelligent Systems XIX (ES 2002). BCS Conference Series, pp. 323–336. Springer, Heidelberg (2002)Google Scholar