Although artificial neural networks have been applied in a variety of real-world scenarios with remarkable success, they have often been criticized for exhibiting a low degree of human comprehensibility. Techniques that compile compact sets of symbolic rules out of artificial neural networks offer a promising perspective to overcome this obvious deficiency of neural network representations.
This paper presents an approach to the extraction of if-then rules from artificial neural networks. Its key mechanism is validity interval analysis, which is a generic tool for extracting symbolic knowledge by propagating rule-like knowledge through Backpropagation-style neural networks. Empirical studies in a robot arm domain illustrate the appropriateness of the proposed method for extracting rules from networks with real-valued and distributed representations.
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@INPROCEEDINGS{Thrun95b,
AUTHOR = {S. Thrun},
YEAR = {1995},
TITLE = {Extracting Rules from Artificial Neural Networks with Distributed Representations},
BOOKTITLE = {Advances in Neural Information Processing Systems
(NIPS) 7},
EDITOR = {G. Tesauro and D. Touretzky and T. Leen},
PUBLISHER = {MIT Press},
ADDRESS = {Cambridge, MA}
}