Abstract
Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper On the proper treatment of connectionism. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andrews, R., Diederich, J., Tickle, A.B.: A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based Systems 8(6), 373–389 (1995)
Bader, S., d’Avila Garcez, A.S., Hitzler, P.: Computing first order logic programs by fibring artificial neural networks. In: Proceedings of International FLAIRS Conference, Florida, USA. AAAI Press, Menlo Park (2005)
Bologna, G.: Is it worth generating rules from neural network ensembles? In: d’Avila Garcez, A.S., Gabbay, D., Hölldobler, S., Taylor, J.G. (eds.) Journal of Applied Logic, Special issue on Neural-Symbolic Integration, vol. 2(3), pp. 325–348 (2004)
Cloete, I., Zurada, J.M. (eds.): Knowledge-Based Neurocomputing. MIT Press, Cambridge (2000)
d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extraction from trained neural networks: A sound approach. Artificial Intelligence 125, 155–207 (2001)
d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Neural-Symbolic Learning Systems: Foundations and Applications. Perspectives in Neural Computing. Springer, Heidelberg (2002)
d’Avila Garcez, A.S., Gabbay, D.M.: Fibring neural networks. In: Proceedings of 19th National Conference on Artificial Intelligence AAAI 2004, San Jose, California, USA, pp. 342–347. AAAI Press, Menlo Park (2004)
d’Avila Garcez, A.S., Lamb, L.C.: Reasoning about time and knowledge in neural-symbolic learning systems. In: Thrun, S., Saul, L., Schoelkopf, B. (eds.) Proceedings of the NIPS 2003 Conference, Vancouver, Canada. Advances in Neural Information Processing Systems, vol. 16, pp. 921–928. MIT Press, Cambridge (2004)
d’Avila Garcez, A.S., Lamb, L.C., Broda, K., Gabbay, D.M.: Applying connectionist modal logics to distributed knowledge representation problems. International Journal of Artificial Intelligence Tools 13(1), 115–139 (2004)
Gabbay, D.M.: Elementary Logics: a Procedural Perspective. Prentice-Hall, Englewood Cliffs (1998)
Gabbay, D.M.: Fibring Logics. Oxford University Press, Oxford (1999)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
Hitzler, P., Hölldobler, S., Seda, A.: Logic programs and connectionist networks. Journal of Applied Logic, Special issue on Neural-Symbolic Integration 2(3), 245–272 (2004); d’Avila Garcez, A.S., Gabbay, D., Hölldobler, S., Taylor, J.G. (eds.)
Hölldobler, S.: Automated inferencing and connectionist models. Postdoctoral Thesis, Intellektik, Informatik, TH Darmstadt (1993)
Hölldobler, S., Kalinke, Y., Storr, H.P.: Approximating the semantics of logic programs by recurrent neural networks. In: Kurfess, F. (ed.) Applied Intelligence Journal, Special Issue on Neural Networks and Structured Knowledge, vol. 11(1), pp. 45–58 (1999)
Lloyd, J.W.: Logic for Learning: Learning Comprehensible Theories from Structured Data. Springer, Heidelberg (2003)
McCarthy, J.: Epistemological challenges for connectionism. Behavior and Brain Sciences 11(1), 44 (1988)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Setiono, R.: Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Computation 9, 205–225 (1997)
Shastri, L.: Advances in SHRUTI: a neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony. In: Kurfess, F. (ed.) Applied Intelligence Journal, Special Issue on Neural Networks and Structured Knowledge, vol. 11, pp. 79–108 (1999)
Shavlik, J.W.: An overview of research at Wisconsin on knowledge-based neural networks. In: Proceedings of the International Conference on Neural Networks ICNN 1996, Washington, DC, pp. 65–69 (1996)
Smolensky, P.: On the proper treatment of connectionism. Behavioral and Brain Sciences 11, 1–74 (1988)
Sun, R.: Robust reasoning: integrating rule-based and similarity-based reasoning. Artificial Intelligence 75(2), 241–296 (1995)
Thrun, S.B.: Extracting provably correct rules from artificial neural networks. Technical report, Institut fur Informatik, Universitat Bonn (1994)
Valiant, L.G.: Three problems in computer science. Journal of the ACM 50(1), 96–99 (2003)
Vardi, M.Y.: Why is modal logic so robustly decidable. In: Immerman, N., Kolaitis, P. (eds.) Descriptive Complexity and Finite Models. Discrete Mathematics and Theoretical Computer Science, vol. 31, pp. 149–184 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcez, A.S.d. (2005). Fewer Epistemological Challenges for Connectionism. In: Cooper, S.B., Löwe, B., Torenvliet, L. (eds) New Computational Paradigms. CiE 2005. Lecture Notes in Computer Science, vol 3526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494645_18
Download citation
DOI: https://doi.org/10.1007/11494645_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26179-7
Online ISBN: 978-3-540-32266-5
eBook Packages: Computer ScienceComputer Science (R0)