Issue No. 03 - May-June (1997 vol. 12)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.590075
<p>Many industrial processes are not understood enough and are too complex for inductive learning methods. The author's technique combines qualitative and memory-based reasoning to model and predict such processes. He has applied this technique to coffee roasting and decaffeination.</p> <p>At the Swiss Federal Institute of Technology's Artificial Intelligence Laboratory, we have developed an approach that combines qualitative reasoning and memory-based reasoning, thereby exploiting their strengths and compensating for their weaknesses. We have applied this approach to two processes: coffee roasting and decaffeination. Both applications used large amounts of data collected from plants operated by Nestle in the UK and Spain. Roasting and decaffeination are processes for which existing models can provide only very inaccurate predictions. In both cases, attempts to predict behavior using statistical methods and neural networks have not provided usable predictions. In contrast, the qualitative models used in memory-based reasoning take into account subtleties of the processes that purely statistical criteria are likely to miss. The results are thus significantly better than what conventional methods could produce. </p>
B. Faltings, "Qualitative Models as Indices for Memory-Based Prediction," in IEEE Intelligent Systems, vol. 12, no. , pp. 47-53, 1997.