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Knowledge Discovery in International Conflict Databases

Johannes Fürnkranz, Johann Petrak, Robert Trappl

Artificial Intelligence is heavily supported by military institutions,while practically no effort goes into the investigation of possible contributions of AI to the avoidance and termination of crises and wars. This paper makes a first step into this direction by investigating the use of machine learning techniques for discovering knowledge in international conflict and conflict management databases. We have applied similarity-based case retrieval to the KOSIMO database of international conflicts. Furthermore, we present results of analyzing the CONFMAN database of successful and unsuccessful conflict management attempts with an inductive decision tree learning algorithm. The latter approach seems to be particularly promising, as conflict management events apparently are more repetitive and thus better suited for machine-aided analysis.

Keywords: Machine Learning, Data Mining, Knowledge Discovery, Peace

Citation: Fürnkranz J., Petrak J., Trappl R.: Knowledge Discovery in International Conflict Databases, Applied Artificial Intelligence 11(2):91-118, March 1997.