• DocumentCode
    446112
  • Title

    Artificial intelligence for conflict management

  • Author

    Habtemariam, E. ; Marwala, T. ; Lagazio, M.

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Univ. of the Witwatersrand, Johannesburg, South Africa
  • Volume
    4
  • fYear
    2005
  • fDate
    July 31 2005-Aug. 4 2005
  • Firstpage
    2583
  • Abstract
    Militarised conflict is one of the risks that have a significant impact on society. Militarised interstate dispute (MID) is defined as an outcome of interstate interactions which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support vector machines (SVMs) have proven themselves to be very good prediction techniques and are introduced for the prediction of MIDs in this study. The results found show that SVM predicts MID better than NN while NN gives more consistent and easy to interpret sensitivity analysis results than SVM.
  • Keywords
    artificial intelligence; neural nets; social sciences computing; support vector machines; artificial intelligence; conflict management; decision support tool; militarised interstate dispute; neural networks; sensitivity analysis; support vector machines; Africa; Artificial intelligence; Artificial neural networks; Biological neural networks; Machine learning; Neural networks; Probability; Sensitivity analysis; Statistical analysis; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
  • Conference_Location
    Montreal, Que.
  • Print_ISBN
    0-7803-9048-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.2005.1556310
  • Filename
    1556310