An overview of hybrid neural systems

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 1778, Issue , 2000, Pages 1-13

  Wermter, Stefan ^a   Sun, Ron ^b  

^a UNIVERSITY OF SUNDERLAND   (United Kingdom)

^b UNIVERSITY OF MISSOURI   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

COMPUTATIONAL SYSTEM; NEURAL SYSTEMS; SYMBOLIC INTERPRETATION;

NEURAL NETWORKS;

EID: 84942900046     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/10719871_1     Document Type: Conference Paper

References (95)

1. R. Andrews, J. Diederich, and A. B. Tickle. A survey and critique of techniques for extracting rules from trained artificial networks. Technical report, Queensland University of Technology, 1995.
2. R. Andrews and S. Geva. Rules and local function networks. In Proceedings of the Rule Extraction From Trained Artificial Neural Networks Workshop, Artificial Intelligence and Simulation of Behaviour, Brighton UK, 1996.
3. J. Barnden. Complex symbol-processing in Conposit. In R. Sun and L. Bookman, editors, Architectures incorporating neural and symbolic processes. Kluwer, Boston, 1994.
4. J. A. Barnden and K. J. Holyoak, editors. Advances in connectionist and neural computation theory, volume 3. Ablex Publishing Corporation, 1994.
5. J. Benitz, J. Castro, and J. I. Requena. Are artificial neural networks black boxes? IEEE Transactions on Neural Networks, 8(5):1156-1164, 1997.
6. R. Bogacz and C. Giraud-Carrier. A novel modular neural architecture for rule-based and similarity-based reasoning. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
7. G. Bologna. Symbolic rule extraction form the DIMLP neural network. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
8. G. Bologna and C. Pellegrini. Accurate decomposition of standard MLP classification responses into symbolic rules. In International Work Conference on Artificial and Natural Neural Networks, IWANN'97, pages 616-627, Lanazrote, Canaries, 1997.
9. Y. Cheng, P. Fortier, and Y. Normandin. A system integrating connectionist and symbolic approaches for spoken language understanding. In Proceedings of the International Conference on Spoken Language Processing, pages 1511-1514, Yokohama, 1994.
10. P. S. Churchland and T. J. Sejnowski. The Computational Brain. MIT Press, Cambridge, MA, 1992.
11. T. Corbett-Clarke and L. Tarassenko. A principled framework and technique for rule extraction from multi-layer perceptrons. In Proceedings of the 5th International Conference on Artificial Neural Networks, pages 233-238, Cambridge, England, July 1997.
12. J. Diederich and D. L. Long. Efficient question answering in a hybrid system. In Proceedings of the International Joint Conference on Neural Networks, Singapore, 1992.
13. G. Dorffner. Neural Networks and a New AI. Chapman and Hall, London, UK, 1997.
14. M. A. Fanty. Learning in structured connectionist networks. Technical Report 252, University of Rochester, Rochester, NY, 1988.
15. J. Feldman and D. Bailey. Layered hybrid connectionist models for cognitive science. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
16. J. A. Feldman and D. H. Ballard. Connectionist models and their properties. Cognitive Science, 6:205-254, 1982.
17. 0-the first five years of an automated language acquisition project. AI Review, 8, 1996.
18. P. Frasconi, M. Gori, and A. Sperduti. Integration of graphical rules with adaptive learning of structured information. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
19. L.M. Fu. Rule learning by searching on adapted nets. In Proceedings of the National Conference on Artificial Intelligence, pages 590-595, 1991.
20. L.M. Fu. Neural Networks in Computer Intelligence. McGraw-Hill, Inc., New York, NY, 1994.
21. S. I. Gallant. Neural Network Learning and Expert Systems. MIT Press, Cambridge, MA, 1993.
22. S. I. Gallant. Context vectors: a step toward a grand unified representation. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
23. J. Gelfand, D. Handleman, and S. Lane. Integrating knowledge-based systems and neural networks for robotic skill. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 193-198, San Mateo, CA., 1989.
24. L. Giles and C. W. Omlin. Extraction, insertion and refinement of symbolic rules in dynamically driven recurrent neural networks. Connection Science, 5:307-337, 1993.
25. S. Goonatilake and S. Khebbal. Intelligent Hybrid Systems. Wiley, Chichester, 1995.
26. N. A. Hallack, G. Zaverucha, and V. C. Barbosa. Towards a hybrid model of first-order theory refinement. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
27. J. A. Hammerton and B. L. Kalman. Holistic symbol computation and the sequential RAAM: An evaluation. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
28. J. Hendler. Developing hybrid symbolic/connectionist models. In J. A. Barnden and J. B. Pollack, editors, Advances in Connectionist and Neural Computation Theory, Vol.1: High Level Connectionist Models, pages 165-179. Ablex Publishing Corporation, Norwood, NJ, 1991.
29. M. Hilario. An overview of strategies for neurosymbolic integration. In Proceedings of the Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, pages 1-6, Montreal, 1995.
30. S. Holldobler. A structured connectionist unification algorithm. In Proceedings of the National Conference of the American Association on Artificial Intelligence 90, pages 587-593, Boston, MA, 1990.
31. S. Holldobler, Y. Kalinke, and J. Wunderlich. A recursive neural network for reflexive reasoning. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
32. S. Honkela. Self-organizing maps in symbol processing. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
33. J. S. R. Jang and C. T. Sun. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions Neural Networks, 4(1):156-159, 1993.
34. D. Jurafsky, C. Wooters, G. Tajchman, J. Segal, A. Stolcke, E. Fosler, and N. Morgan. The Berkeley Restaurant Project. In Proceedings of the International Conference on Speech and Language Processing, pages 2139-2142, Yokohama, 1994.
35. P. Kanerva. Large patterns make great symbols: an example of learning from example. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
36. C. Kirkham and T. Harris. Development of a hybrid neural network/expert system for machine health monitoring. In R. Rao, editor, Proceedings of the 8th International Congress on Condition Monitoring and Engineering Management, COMADEM95, pages 55-60, 1995.
37. G.K. Kraetzschmar, S. Sablatnoeg, S. Enderle, and G. Palm. Application of neuro-symbolic integration for environment modelling in mobile robots. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
38. S. C. Kremer. A theory of grammatical induction in the connectionist paradigm. Technical Report PhD dissertation, Dept. of Computing Science, University of Alberta, Edmonton, 1996.
39. S.C. Kremer and J. Kolen. Dynamical recurrent networks for sequential data processing. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
40. F. Kurfeß. Unification on a connectionist simulator. In T. Kohonen, K. Makisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, pages 471-476. North-Holland, 1991.
41. S. C. Kwasny and K. A. Faisal. Connectionism and determinism in a syntactic parser. In N. Sharkey, editor, Connectionist natural language processing, pages 119-162. Lawrence Erlbaum, Hillsdale, NJ, 1992.
42. T. Lange and M. Dyer. High-level inferencing in a connectionist network. Connection Science, 1:181-217, 1989.
43. B. Lees, B. Kumar, A. Mathew, J. Corchado, B. Sinha, and R. Pedreschi. A hybrid case-based neural network approach to scientific and engineering data analysis. In Proceedings of the Eighteenth Annual International Conference of the British Computer Society Specialist Group on Expert Systems, pages 245-260, Cambridge, 1998.
44. H. Lipson and H.T. Siegelmann. High order eigentensors as symbolic rules in competitive learning. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
45. J. MacIntyre and P. Smith. Application of hybrid systems in the power industry. In L. Medsker, editor, Intelligent Hybrid Systems, pages 57-74. Kluwer Academic Press, 1995.
46. M.R. Mayberry and R. Miikkulainen. Combining maps and distributed representations for shift-reduce parsing. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
47. K. McGarry, S. Wermter, and J. MacIntyre. Hybrid neural systems: from simple coupling to fully integrated neural networks. Neural Computing Surveys, 2:62-94, 1999.
48. L. R. Medsker. Hybrid Neural Network and Expert Systems. Kluwer Academic Publishers, Boston, 1994.
49. L. R. Medsker. Hybrid Intelligent Systems. Kluwer Academic Publishers, Boston, 1995.
50. R. Miikkulainen. Subsymbolic Natural Language Processing. MIT Press, Cambridge, MA, 1993.
51. W. C. Morris, G. W. Cottrell, and J. L. Elman. A connectionist simulation of the empirical acquisition of grammatical relations. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
52. M. C. Mozer. Neural net architectures for temporal sequence processing. In A. Wei-gend and N. Gershenfeld, editors, Time series prediction: Forecasting the future and understanding the past, pages 243-264. Addison-Wesley, Redwood City, CA, 1993.
53. C. W. Omlin and C. L. Giles. Extraction and insertion of symbolic information in recurrent neural networks. In V. Honavar and L. Uhr, editors, Artificial Intelligence and Neural Networks:Steps Towards principled Integration, pages 271-299. Academic Press, San Diego, 1994.
54. C. W. Omlin and C. L. Giles. Extraction of rules from discrete-time recurrent neural networks. Neural Networks, 9(1):41-52, 1996.
55. C.W. Omlin, L. Giles, and K. K. Thornber. Fuzzy knowledge and recurrent neural networks: A dynamical systems perspective. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
56. C. Orovas and J. Austin. A cellular neural associative array for symbolic vision. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
57. J. Park and I. W. Sandberg. Universal approximation using radial basis function networks. Neural Computation, 3:246-257, 1991.
58. N. S. Park. Addressing knowledge representation issues in connectionist symbolic rule encoding for general inference. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
59. T. Peterson and R. Sun. An RBF network alternative for a hybrid architecture. In International Joint Conference on Neural Networks, Ancorage, AK, May 1998.
60. J. B. Pollack. Recursive distributed representations. Artificial Intelligence, 46:77-105, 1990.
61. R. Reilly. Evolution of symbolisation: Signposts to a bridge between connectionist and symbolic systems. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
62. R. G. Reilly and N. E. Sharkey. Connectionist Approaches to Natural Language Processing. Lawrence Erlbaum Associates, Hillsdale, NJ, 1992.
63. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, pages 318-362. MIT Press, Cambridge, MA, 1986.
64. N. Sharkey and N. T. Ziemke. Life, mind and robots: The ins and outs of embodied cognition. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
65. L. Shastri. A model of rapid memory formation in the hippocampal system. In Proceedings of the Meeting of the Cognitive Science Society, pages 680-685, Stanford, 1997.
66. L. Shastri. Types and quantifiers in SHRUTI: a connectionist model of rapid reasoning and relational processing. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
67. L. Shastri and V. Ajjanagadde. From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings. Behavioral and Brain Sciences, 16(3):417-94, 1993.
68. J. Shavlik. A framework for combining symbolic and neural learning. In V. Honavar and L. Uhr, editors, Artificial Intelligence and Neural Networks: Steps towards principled Integration, pages 561-580. Academic Press, San Diego, 1994.
69. P. Smolensky. On the proper treatment of connnectionism. Behavioral and Brain Sciences, 11(1):1-74, March 1988.
70. A. Sperduti, A. Starita, and C. Goller. Learning distributed representations for the classifications of terms. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 494-515, Montreal, 1995.
71. R. Sun. On variable binding in connectionist networks. Connection Science, 4(2):93-124, 1992.
72. R. Sun. Integrating Rules and Connectionism for Robust Commonsense Reasoning. Wiley, New York, 1994.
73. R. Sun. Hybrid connectionist-symbolic models: A report from the IJCAI95 workshop on connectionist-symbolic integration. Artificial Intelligence Magazine, 1996.
74. R. Sun. Supplementing neural reinforcement learning with symbolic methods. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
75. R. Sun and F. Alexandre. Proceedings of the Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. McGraw-Hill, Inc., Montreal, 1995.
76. R. Sun and F. Alexandre. Connectionist Symbolic Integration. Lawrence Erlbaum Associates, Hillsdale, NJ, 1997.
77. R. Sun and L.A. Bookman. Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Boston, MA, 1995.
78. R. Sun and T. Peterson. Autonomous learning of sequential tasks: experiments and analyses. IEEE Transactions on Neural Networks, 9(6):1217-1234, 1998.
79. S. Thrun. Extracting rules from artificial neural networks with distributed representations. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7. MIT Press, San Mateo, CA, 1995.
80. S. Thrun. Explanation-Based Neural Network Learning. Kluwer, Boston, 1996.
81. A. Tickle, F. Maire, G. Bologna, R. Andrews, and J. Diederich. Lessons from past, current issues and future research directions in extracting the knowledge embedded in artificial neural networks. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
82. P. Tino, G. Dorffner, and C. Schittenkopf. Understanding state space organization in recurrent neural networks with iterative function systems dynamics. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
83. H. Tirri. Replacing the pattern matcher of an expert system with a neural network. In S. Goonatilake and S. Khebbal, editors, Intelligent Hybrid Systems, pages 47-62. John Wiley and Sons, 1995.
84. M.L. Vaughn, S.J. Cavill, S.J. Taylor, M.A. Foy, and A.J.B. Fogg. Direct knowledge extraction and interpretation from a multilayer perceptron network that performs low back pain classification. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
85. D. Waltz. The importance of importance. In Presentation at Workshop on Hybrid Neural Symbolic Integration, Breckenridge, CO., 1998.
86. D. L. Waltz and J. A. Feldman. Connectionist Models and their Implications. Ablex, 1988.
87. S. Wermter. Hybrid Connectionist Natural Language Processing. Chapman and Hall, Thomson International, London, UK, 1995.
88. S. Wermter. Preference Moore machines for neural fuzzy integration. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 840-845, Stockholm, 1999.
89. S. Wermter. The hybrid approach to artificial neural network-based language processing. In R. Dale, H. Moisl, and H. Somers, editors, A Handbook of Natural Language Processing. Marcel Dekker, 2000.
90. S. Wermter. Knowledge extraction from transducer neural networks. Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Techniques, 12:27-42, 2000.
91. S. Wermter, G. Arevian, and C. Panchev. Towards hybrid neural learning internet agents. In Hybrid Neural Systems (this volume). Springer-Verlag, 2000.
92. S. Wermter and M. Meurer. Building lexical representations dynamically using artificial neural networks. In Proceedings of the International Conference of the Cognitive Science Society, pages 802-807, Stanford, 1997.
93. S. Wermter, C. Panchev, and G. Arevian. Hybrid neural plausibility networks for news agents. In Proceedings of the National Conference on Artificial Intelligence, pages 93-98, Orlando, USA, 1999.
94. S. Wermter, E. Riloff, and G. Scheler. Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Springer, Berlin, 1996.
95. S. Wermter and V. Weber. SCREEN: Learning a flat syntactic and semantic spoken language analysis using artificial neural networks. Journal of Artificial Intelligence Research, 6(1):35-85, 1997.