Rule Revision With Recurrent Neural Networks

Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly-generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect (i.e. the rules were not the ones in the randomly generated grammar).

[1]  Giovanni Soda,et al.  Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks , 1995, IEEE Trans. Knowl. Data Eng..

[2]  Irving S. Reed,et al.  Including Hints in Training Neural Nets , 1991, Neural Computation.

[3]  C. Lee Giles,et al.  Constructing deterministic finite-state automata in recurrent neural networks , 1996, JACM.

[4]  Peter Tiño,et al.  Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model , 1995, Neural Comput..

[5]  Raymond L. Watrous,et al.  Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.

[6]  C. L. Giles,et al.  Heuristics for the extraction of rules from discrete-time recurrent neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[7]  C. Lee Giles,et al.  Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants , 1996, Neural Computation.

[8]  C. L. Giles,et al.  Inserting rules into recurrent neural networks , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[9]  Panagiotis Manolios,et al.  First-Order Recurrent Neural Networks and Deterministic Finite State Automata , 1994, Neural Computation.

[10]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[11]  C. L. Giles,et al.  Second-order recurrent neural networks for grammatical inference , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[12]  Giovanni Soda,et al.  An unified approach for integrating explicit knowledge and learning by example in recurrent networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[13]  C. Lee Giles,et al.  Training Second-Order Recurrent Neural Networks using Hints , 1992, ML.

[14]  Hamid R. Berenji,et al.  Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning , 1991, ML.

[15]  Colin Giles,et al.  Learning, invariance, and generalization in high-order neural networks. , 1987, Applied optics.

[16]  Steven C. Suddarth,et al.  Symbolic-Neural Systems and the Use of Hints for Developing Complex Systems , 1991, Int. J. Man Mach. Stud..

[17]  Eduardo Sontag,et al.  Turing computability with neural nets , 1991 .

[18]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[19]  Allen Ginsberg,et al.  Theory Revision via Prior Operationalization , 1988, AAAI.

[20]  C. Lee Giles,et al.  Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.

[21]  Paulo J. G. Lisboa,et al.  Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers , 1992, IEEE Trans. Neural Networks.

[22]  Pierre Roussel-Ragot,et al.  Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms , 1993, Neural Computation.

[23]  Michael J. Pazzani.  Detecting and Correcting Errors of Omission After Explanation-Based Learning , 1989, IJCAI.

[24]  C. Lee Giles,et al.  Extraction of rules from discrete-time recurrent neural networks , 1996, Neural Networks.

[25]  Yaser S. Abu-Mostafa,et al.  Learning from hints in neural networks , 1990, J. Complex..

[26]  Richard Maclin,et al.  Refining algorithms with knowledge-based neural networks: improving the Chou-Fasman algorithm for protein folding , 1994, COLT 1994.

[27]  C. L. Giles,et al.  Constructing deterministic finite-state automata in sparse recurrent neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[28]  Michael C. Mozer,et al.  A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction , 1993, NIPS.