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A distributed problem-solving approach for owner-contractor prequalification

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  • Sang Chan Park
  • Jeffrey Russell
  • Mahmoud Taha

Abstract

This paper reports the results of work that aims to study the practical implementation of Distributed Artificial Intelligence (DAI) and to introduce its capabilities in representing and using knowledge in the area of contractor prequalification. This is accomplished by developing an intelligent Knowledge-Based Decision Support System for a contractor prequalification environment. The resulting DAI architecture comprises a hierarchy of loosely coupled problem solvers, all operating under the supervision of a top-level control mechanism. Each problem solver works as a classifier system. The problem-solving knowledge for each problem solver is developed using (1) machine learning by training neural networks, and (2) heuristic rules of thumb. A learning and refining subsystem is attached to the system for refining the existing knowledge to improve the system performance. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Sang Chan Park & Jeffrey Russell & Mahmoud Taha, 1998. "A distributed problem-solving approach for owner-contractor prequalification," Annals of Operations Research, Springer, vol. 78(0), pages 111-125, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:111-125:10.1023/a:1018941815032
    DOI: 10.1023/A:1018941815032
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    Cited by:

    1. Shrey Jain & Sunil Kumar Jauhar & Piyush, 2024. "A machine-learning-based framework for contractor selection and order allocation in public construction projects considering sustainability, risk, and safety," Annals of Operations Research, Springer, vol. 338(1), pages 225-267, July.

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