ITTC Project

Using Case-Based Reasoning (CBR) to Identify and Correct Errors in Integrated Waybills

Project Award Date: 08-01-2001


Case Based reasoning (CBR) is both a problem-solving methodology as well as a theory of reasoning and memory based on the experiences of human reasoning. A CBR system is an intelligent, problem solving system that reasons by retrieving relevant cases from its memory. It then adapts the solution of the old situation to solve the new problem. The aim of this project is to apply CBR to the identification and correction of errors in integrated waybills of Burlington Northern Santa Fe (BNSF).

With aid from BNSF, the features of a waybill and the domain-specific similarity function will be identified. More over, the case representation and similarity function using existing CBR code will be implemented. Subsequently, various CBR scenarios will be analyzed in order to decide the best fit for its application. This analysis may include possible scenarios such as: If the system compares the current waybill to its case of older waybills, and finds similar cases in which corrections were needed to assure payment, then the system will automatically correct the waybill.

This project is in partnership with various parties. BNSF may include other parties in the overall Case-Based Reasoning project, where specialized knowledge or expertise exists. The KU researchers will cooperate with the other parties involved in the work to achieve the overall goals for the study and application of CBR to BNSFs problems.


Faculty Investigator(s): Costas Tsatsoulis (PI)

Project Sponsors

Primary Sponsor(s): Burlington Northern, Inc.

Partner with ITTC

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