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Prediction of Construction Litigation Outcome – A Case-Based Reasoning Approach

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Advances in Applied Artificial Intelligence (IEA/AIE 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4031))

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Abstract

Since construction claims are normally affected by a large number of complex and interrelated factors, it will be advantageous to the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The application of recent artificial intelligence technologies can be cost-effective in this problem domain. In this paper, a case-based reasoning (CBR) approach is adopted to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show that the CBR system is able to give a successful prediction rate higher than 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.

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References

  1. Chau, K.W.: Resolving Construction Disputes by Mediation: Hong Kong Experience. Journal of Management in Engineering, ASCE 8(4), 384–393 (1992)

    Article  MathSciNet  Google Scholar 

  2. Arditi, D., Oksay, F.E., Tokdemir, O.B.: Predicting the Outcome of Construction Litigation Using Neural Networks. Computer-Aided Civil and Infrastructure Engineering 13(2), 75–81 (1998)

    Article  Google Scholar 

  3. Chau, K.W.: Predicting Construction Litigation Outcome using Particle Swarm Optimization. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 571–578. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Barletta, R.: An Introduction to Case-Based Reasoning. AI Expert 6(8), 42–49 (1991)

    Google Scholar 

  5. Kolodner, J.L.: An Introduction to Case-Based Reasoning. Artificial Intelligence Review 6(1), 3–34 (1992)

    Article  Google Scholar 

  6. Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AICOM 7(1), 39–59 (1994)

    Google Scholar 

  7. Gupta, U.G.: How Case-Based Reasoning solves New Problems. Interfaces 24(6), 110–119 (1994)

    Article  Google Scholar 

  8. Watson, I., Marir, F.: Case-Based Reasoning: A Review. The Knowledge Engineering Review 9(4), 327–354 (1994)

    Article  Google Scholar 

  9. Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Chau, K.W. (2006). Prediction of Construction Litigation Outcome – A Case-Based Reasoning Approach. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_59

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  • DOI: https://doi.org/10.1007/11779568_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35453-6

  • Online ISBN: 978-3-540-35454-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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