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This is an extended report focussing on experimental results to explore the necessity of user guidance in case-based knowledge acquisition. It is covering a collection of theoretical investigations as... well. The methodology of our approach is quite simple: We choose a well-understood area which is tailored to case-based knowledge acquisition. Furthermore, we choose a prototypical case-based learning algorithm which is obviously suitable for the problem domain under consideration. Then, we perform a number of knowledge acquisition experiments. They clearly exhibit essential limitations of knowledge acquisition from randomly chosen cases. As a consequence, we develop scenarios of user guidance. Based on these theoretical concepts, we prove a few theoretical results characterizing the power of our approach. Next, we perform a new series of more constrained results which support our theoretical investigations. The main experiments deal with the difficulties of learning from randomly arranged data in 4 different formal settings. The key insight is that even the right data do not suffice, if they are not arranged appropriately. The present report aims at presenting a large amount of experimental data exceeding the space available in conference proceedings, usually. We are reporting more than a million of individual learning experiments, each of them comprising several steps of generating hypotheses (2 500 per run, in some cases). First results have been presented at the 1996 Pacific Knowledge Acquisition Workshop in Sydney, Australia. A much shorter version of this report will be presented on FLAIRS-97, the Florida AI Research Symposium in Daytona Beach, FL, USA, May 1997.続きを見る
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Mendeley出力
trcs135
trcs135.ps