Answering with Cases: A CBR Approach to Deep Learning

Kareem Amin, Stelios Kapetanakis, Klaus-Dieter Althoff, Andreas Dengel, Miltos Petridis

In: Michael T. Cox, Peter Funk, Shahina Begum (Hrsg.). Case-Based Reasoning Research and Development. International Conference on Case-Based Reasoning (ICCBR-2018) July 9-12 Stockholm Sweden Springer 12/2018.


Every year tenths of thousands of customer support engineers around the world deal with, and proactively solve, complex help-desk tickets. Daily, almost every customer support expert will turn his/her attention to a prioritization strategy, to achieve the best possible result. To assist with this, in this paper we describe a novel case-based reasoning application to address the tasks of: high solution accuracy and shorter prediction resolution time. We describe how appropriate cases can be generated to assist engineers and how our solution can scale over time to produce domain-specifi c reusable cases for similar problems. Our work is evaluated using data from 5000 cases from the automotive industry.

Weitere Links

paper_7.pdf (pdf, 301 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence