Extracting Rules from Artificial Neural Networks with Distributed Representations

Sebastian Thrun

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.

Click here to obtain the full paper (91817 bytes).

@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}
}