Skip to main content

Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them

  • Conference paper
Learning, Networks and Statistics

Part of the book series: International Centre for Mechanical Sciences ((CISM,volume 382))

Abstract

In a current project we investigate the potential contribution of Artificial Intelligence for the avoidance and termination of crises and wars. This paper reports some results obtained by analyzing international conflict databases using machine learning and case-based reasoning techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Azar, E. E. (1980). The conflict and peace data bank (COPDAB) project. Journal of Conflict Resolution, 24(1), 143–152.

    Google Scholar 

  • Bercovitch, J., Houston, A. (1993). Influence of mediation characteristics and behavior on the success of mediation in international relations. The International Journal of Conflict Management, 4 (4), 297–321.

    Article  Google Scholar 

  • Bercovitch, J., Lamare, J. W. (1993). The process of international mediation: An analysis of the determinants of successful and unsuccessful outcomes. Australian Journal of Political Science, 28, 290–305.

    Article  Google Scholar 

  • Bercovitch, J., Langley, J. (1993). The nature of dispute and the effectiveness of international mediation. Journal of Conflict Resolution,. 97 (4), 670–691.

    Article  Google Scholar 

  • Bercovitch, J., Wells, R. (1993). Evaluating mediation strategies–a theoretical and empirical analysis. Peace, Change, 18(1), 3–25.

    Google Scholar 

  • Bond, D., Bennet, B., Voegele, W. B. (1994). Panda: Interaction events data development using automated human coding.. Extended Version of a paper presented at the 1994 Annual Meeting of the International Studies Association in Washington, DC on April 1st, 1994.

    Google Scholar 

  • Brecher, M., Wilkenfeld, J., Moser, S. (1988). Crises in the Twentieth Century - Handbook of International Crises, Vol. I. Pergamon Press, Oxford.

    Google Scholar 

  • Butterworth, R. L. (1976). Managing Interstate Conflict, 1945–74: Data with Synopses. University of Pittsburgh Center for International Studies, Pittsburgh.

    Google Scholar 

  • Fürnkranz, J., Petrak, J., Trappl, R. (1997). Knowledge discovery in international conflict databases. Applied Artificial Intelligence, 11(2), 91–118.

    Google Scholar 

  • Gantzel, K. J., Meyer-Stamer, J. (Eds.). (1986). Die Kriege nach dem. weiten Weltkrieg bis 1984. München.

    Google Scholar 

  • Gochman, C. S., Maoz, Z. (1984). Militarized interstate disputes 1816–1976: Procedures, patterns, and insights. Journal of Conflict Resolution, 28, 585–616.

    Article  Google Scholar 

  • Holsti, K. J. (1983). International Politics: A Framework for Analysis (2nd edition). Englewood Cliffs.

    Google Scholar 

  • Hudson, V. M. (1991). Artificial Intelligence and International Politics. West-view Press, Boulder, CO.

    Google Scholar 

  • John, G. H., Kohavi, R., Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Cohen, W., Hirsh, H. (Eds.), Proceedings of the 11th International Conference on Machine Learning (ML-94), pp. 121–129 New Brunswick, NJ. Morgan Kaufmann.

    Google Scholar 

  • Kohavi, R., John, G., Manley, D., Pfleger, K. (1994). MLC++: A machine learning library in C++.. In Tools with Artificial Intelligence. IEEE Computer Society Press.

    Google Scholar 

  • Kononenko, I., Bratko, I. (1991). Information-based evaluation criterion for classifier’s performance. Machine Learning, 6, 67–80.

    Google Scholar 

  • Mallery, J. C. (1988). Thinking about foreign policy: Finding an appropriate role for artificial intelligence computers. Master’s thesis, M. I. T. Political Science Department, Cambridge, MA.

    Google Scholar 

  • Mallery, J. C., Sherman, F. L. (1993). Learning historical rules of major power intervention in the post-war international system.. Paper prepared for presentation at the 1993 Annual Meeting of the International Studies Association.

    Google Scholar 

  • Petrak, J. (1994). VIE-CBR - Vienna case-based reasoning tool, version 1.0: Programmer’s and installation manual. Technical report TR-94–34, Austrian Research Institute for Artificial Intelligence, Vienna. Available from http://ftp.ai.univie.ac.atpapers/oefai-tr-94–34.ps.Z.

    Google Scholar 

  • Petrak, J., Trapp’, R., Fürnkranz, J. (1994). The possible contribution of AI to the avoidance of crises and wars: Using CBR methods with the KOSIMO database of conflicts. Technical report TR-94–32, Austrian Research Institute for Artificial Intelligence, Vienna. Available from http://ftp.ai.univie.ac.at/papers/oefai-tr-94–32.ps.Z.

    Google Scholar 

  • Pfetsch, F. R., Billing, P. (1994). Datenhandbuch nationaler und internationaler Konflikte. Nomos Verlagsgesellschaft, Baden-Baden.

    Google Scholar 

  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Schrodt, P. A. (1991a). Artificial intelligence and international relations: An overview. In (Hudson, 1991 ).

    Google Scholar 

  • Schrodt, P. A. (1991b). Classification of interstate conflict outcomes using a bootstrapped ID3 algorithm. Political Analysis.

    Google Scholar 

  • Schrodt, P. A. (1996). Patterns, Rules and Learning: Computational Models of International Behavior. University of Michigan Press. Forthcoming.

    Google Scholar 

  • Schrodt, P. A., Davis, S. G. (1994). Techniques and troubles in the machine coding of international event data. Dept. of Political Science, University of Kansas. Paper presented at the 1994 meeting of the International Studies Association, Washington DC.

    Google Scholar 

  • Sherman, F. L. (1988). Sherfacs: A new cross-paradigm, international conflict dataset.. Paper written for presentation at the 1988 annual meeting of the International Studies Association.

    Google Scholar 

  • Trappl, R. (1986). Reducing international tension through artificial intelligence: A proposal for 3 projects. In Trappl, R. (Ed.), Power, Autonomy, Utopia: New Approaches Toward Complex Systems. Plenum, New York.

    Chapter  Google Scholar 

  • Trappl, R. (1992). The role of artificial intelligence in the avoidance of war. In Trappl, R. (Ed.), Cybernetics and Systems ‘82, pp. 1667–1672 Singapore. World Scientific.

    Google Scholar 

  • Unseld, S. D., Mallery, J. C. (1993). Interaction detection in complex data-models.. MIT A.I. Memo No. 1298.

    Google Scholar 

  • Vogele, W. B. (1994). Global conflict profiles: Event data analysis using panda.. Paper prepared for presentation at the annual meeting of the American Political Science Association, New York City, Sep. 1994.

    Google Scholar 

  • Wilkenfeld, J., Brecher, M., Moser, S. (1988). Crises in the Twentieth Century - Handbook of Foreign Policy Crises, Vol. II. Pergamon Press, Oxford.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Wien

About this paper

Cite this paper

Trappl, R., Fürnkranz, J., Petrak, J., Bercovitch, J. (1997). Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Learning, Networks and Statistics. International Centre for Mechanical Sciences, vol 382. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2668-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-2668-4_13

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82910-3

  • Online ISBN: 978-3-7091-2668-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics