Connectionist model generation: A first-order approach

Neurocomputing

Volumn 71, Issue 13-15, 2008, Pages 2420-2432

  Bader, Sebastian ^a   Hitzler, Pascal ^b   Hölldobler, Steffen ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

Connectionist model generation; First order logic programs; Neural symbolic integration; Recurrent RBF networks

Indexed keywords

COMPUTER CIRCUITS; DEEP NEURAL NETWORKS; FORMAL LOGIC; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; LOGIC PROGRAMMING; NEURAL NETWORKS;

CONNECTIONIST MODEL GENERATION; EXPERIMENTAL EVALUATION; FIRST-ORDER LOGIC PROGRAMS; KNOWLEDGE REPRESENTATION AND REASONING; KNOWLEDGE-BASED ARTIFICIAL NEURAL NETWORK; NEURAL-SYMBOLIC INTEGRATION; NEURAL-SYMBOLIC SYSTEMS; RECURRENT RBF NETWORKS;

RECURRENT NEURAL NETWORKS;

ACCURACY; ARTIFICIAL NEURAL NETWORK; CONFERENCE PAPER; CONTROLLED STUDY; FEASIBILITY STUDY; KNOWLEDGE; LEARNING ALGORITHM; PRIORITY JOURNAL; READING; SEMANTICS; SYMBOLISM; TASK PERFORMANCE;

EID: 56449117969     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.10.028     Document Type: Conference Paper

References (36)

1. R. Andrews, J. Diederich, A. Tickle, A survey and critique of techniques for extracting rules from trained artificial neural networks, Knowledge-Based Systems (1995) 8 (6).
2. K.R. Apt, H.A. Blair, A. Walker, Towards a theory of declarative knowledge, in: J. Minker (Ed.), Foundations of Deductive Databases and Logic Programming, Morgan Kaufmann, Los Altos, CA, 1988, pp. 89-148.
3. S. Bader, P. Hitzler, Logic programs, iterated function systems, and recurrent radial basis function networks, Journal of Applied Logic 2 (3) (2004) 273-300.
4. S. Bader, P. Hitzler, Dimensions of neural-symbolic integration - a structured survey, in: S. Artemov, H. Barringer, A.S. d'Avila Garcez, L.C. Lamb, J. Woods (Eds.), We Will Show Them: Essays in Honour of Dov Gabbay, King's College Publications, 2005, pp. 167-194.
5. S. Bader, P. Hitzler, S. Hölldobler, A. Witzel, A fully connectionist model generator for covered first-order logic programs, in: M.M. Veloso (Ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, AAAI Press, Menlo Park, CA, January 2007, pp. 666-671.
6. S. Bader, S. Hölldobler, The Core Method: Connectionist model generation, in: Proceedings of the ICANN'06, Lecture Notes in Computer Science, vol. 4132, 2006, pp. 1-13.
7. D.H. Ballard, Parallel logic inference and energy minimization, in: Proceedings of the 5th National Conference on Artificial Intelligence, Philadelphia, PA, August 1986, Morgan Kaufmann, Los Altos, CA, pp. 203-208.
8. M. Barnsley, Fractals Everywhere, Academic Press, San Diego, CA, USA, 1993.
9. C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.
10. A.S. d'Avila Garcez, K.B. Broda, D.M. Gabbay, Neural- Symbolic Learning Systems - Foundations and Applications, Springer, Berlin, 2002.
11. A.S. d'Avila Garcez, L.C. Lamb, D.M. Gabbay, A connectionist inductive learning system for modal logic programming, in: Proceedings of the IEEE International Conference on Neural Information Processing ICONIP'02, Singapore, 2002.
12. A.S. d'Avila Garcez, L.C. Lamb, D.M. Gabbay, Neural-symbolic intuitionistic reasoning, in: M. Koppen A. Abraham, K. Franke (Eds.), Frontiers in Artificial Intelligence and Applications, Melbourne, Australia, December 2003, IOS Press. Proceedings of the Third International Conference on Hybrid Intelligent Systems (HIS'03).
13. A.S. d'Avila Garcez, G. Zaverucha, Luis A.V. de Carvalho, Logical inference and inductive learning in artificial neural networks, in: C. Hermann, F. Reine, A. Strohmaier (Eds.), Knowledge Representation in Neural Networks, Logos Verlag, Berlin, 1997, pp. 33-46.
14. M. Fitting, Metric methods, three examples and a theorem, Journal of Logic Programming 21 (3) (1994) 113-127.
15. B. Fritzke, Vektorbasierte Neuronale Netze, Habilitation, Shaker Verlag, 1998.
16. K.-I. Funahashi, On the approximate realization of continuous mappings by Neural Networks, 2 (1989) 183-192.
17. H. Gust, K.-U. Kühnberger, P. Geibel, Learning models of predicate logical theories with neural networks based on topos theory, in: Hammer, B., Hitzler, P. (Eds.), Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, vol. 77, Springer, Berlin, 2007, pp. 233-264.
18. B. Hammer, P. Hitzler, (Eds.), Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, vol. 77, Springer, Berlin, 2007.
19. P. Hitzler, S. Hölldobler, A.K. Seda, Logic programs and connectionist networks, Journal of Applied Logic 3 (2) (2004) 245-272.
20. S. Hölldobler, Y. Kalinke, Towards a massively parallel computational model for logic programming, in: Proceedings ECAI94 Workshop on Combining Symbolic and Connectionist Processing, ECCAI, 1994, pp. 68-77.
21. S. Hölldobler, Y. Kalinke, H.-P. Störr, Approximating the semantics of logic programs by recurrent neural networks, Applied Intelligence 11 (1999) 45-58.
22. S. Hölldobler, F. Kurfess, CHCL - A connectionist inference system, in: B. Fronhöfer, G. Wrightson (Eds.), Parallelization in Inference Systems, Lecture Notes in Artificial Intelligence, vol. 590, Springer, Berlin, 1992, pp. 318-342.
23. K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (1989) 359-366.
24. Y. Kalinke, Ein massiv paralleles Berechnungsmodell für normale logische Programme, Master's Thesis, TU Dresden, Fakultaät Informatik, 1994 (in German).
25. M. Lane, A.K. Seda, Some aspects of the integration of connectionist and logicbased systems, Information 9 (4) (2006) 551-562.
26. T.E. Lange, M.G. Dyer, High-level inferencing in a connectionist network, Connection Science 1 (1989) 181-217.
27. J.W. Lloyd, Foundations of Logic Programming, Springer, Berlin, 1988.
28. J. McCarthy, Epistemological challanges for connectionism, Behavioural and Brain Sciences 11 (1988) 44 Commentary to [34].
29. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5 (1943) 115-133.
30. G. Pinkas, Symmetric neural networks and logic satisfiability, Neural Computation 3 (1991) 282-291.
31. A.K. Seda, Topology and the semantics of logic programs, Fundamenta Informaticae 24 (4) (1995) 359-386.
32. L. Shastri, SHRUTI: A neurally motivated architecture for rapid, scalable inference, in: Hammer, B., Hitzler, P. (Eds.), Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, vol. 77, Springer, Berlin, 2007, pp. 183-204.
33. L. Shastri, V. Ajjanagadde, From associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony, Behavioural and Brain Sciences 16 (3) (1993) 417-494.
34. P. Smolensky, On variable binding and the representation of symbolic structures in connectionist systems, Technical Report CU-CS-355-87, Department of Computer Science and Institute of Cognitive Science, University of Colorado, Boulder, CO 80309-0430, 1987.
35. G.G. Towell, J.W. Shavlik, Extracting refined rules from knowledge-based neural networks, Machine Learning 13 (1993) 71-101.
36. S. Willard, General Topology, Addison-Wesley, Reading, MA, 1970.