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Ioannis Hatzilygeroudis, Jim Prentzas, "Integrated RuleBased Learning and Inference," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 11, pp. 15491562, November, 2010.  
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@article{ 10.1109/TKDE.2010.79, author = {Ioannis Hatzilygeroudis and Jim Prentzas}, title = {Integrated RuleBased Learning and Inference}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {22}, number = {11}, issn = {10414347}, year = {2010}, pages = {15491562}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.79}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Integrated RuleBased Learning and Inference IS  11 SN  10414347 SP1549 EP1562 EPD  15491562 A1  Ioannis Hatzilygeroudis, A1  Jim Prentzas, PY  2010 KW  Neurosymbolic integration KW  integrated inference KW  rulebased reasoning KW  neurocomputing. VL  22 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] A. Asuncion and D.J. Newman, "UCI Machine Learning Repository," http://www.ics.uci.edu/~mlearnMLRepository.html , School of Information and Computer Science, Univ. of California, 2007.
[2] S. Bader and P. Hitzler, "Dimensions of NeuralSymbolic Integration—A Structured Survey," We Will Show Them: Essays in Honour of Dov Gabbay, S. Artemov, H. Barringer, A.S. d'Avila Garcez, L.C. Lamb, and J. Woods, eds., vol. 1, pp. 167194, College Publications, 2005.
[3] S. Bader, P. Hitzler, S. Holldobler, and A. Witzel, "A Fully Connectionist Model Generator for Covered FirstOrder Logic Programs," Proc. 20th Int'l Joint Conf. Artificial Intelligence (IJCAI '07), pp. 666671, 2007.
[4] S. Bader, P. Hitzler, and S. Holldobler, "Connectionist Model Generation: A FirstOrder Approach," Neurocomputing, vol. 71, pp. 24202432, 2008.
[5] M. Bohanec and B. Zupan, "AI Lab Data Sets," http://magix.fri.unilj.si/blaz/hintdata sets.htm , Faculty of Computer and Information Science, Univ. of Ljubljana, 1997.
[6] G. Bologna, "A Model for Single and Multiple Knowledge Based Networks," Artificial Intelligence in Medicine, vol. 28, no. 2, pp. 141163, 2003.
[7] Connection Science, special issue on integrating neural and symbolic processes, L. Bookman and R. Sun, eds., vol. 5, nos. 3/4, 1993.
[8] A. Browne and R. Sun, "Connectionist Inference Models," Neural Networks, vol. 14, no. 10, pp. 13311355, 2001.
[9] G.A. Carpenter and A.H. Tan, "Rule Extraction: From Neural Architecture to Symbolic Representation," Connection Science, vol. 7, pp. 327, 1995.
[10] KnowledgeBased Neurocomputing, I. Cloete and J.M. Zurada, eds. MIT Press, 2000.
[11] L.M. Fu, "KnowledgeBased Connectionism for Revising Domain Theories," IEEE Trans. Systems, Man, and Cybernetics, vol. 23, no. 1, pp. 173182, Jan./Feb. 1993.
[12] S.I. Gallant, "Connectionist Expert Systems," Comm. ACM, vol. 31, no. 2, pp. 152169, 1988.
[13] S.I. Gallant, Neural Network Learning and Expert Systems. MIT Press, 1993.
[14] A.S. d'Avila Garcez, K. Broda, and D.M. Gabbay, "Symbolic Knowledge Extraction from Trained Neural Networks: A Sound Approach," Artificial Intelligence, vol. 125, nos. 1/2, pp. 155207, 2001.
[15] A. d'Avila Garcez, K. Broda, and D.M. Gabbay, NeuralSymbolic Learning Systems: Foundations and Applications, Perspectives in Neural Computing. SpringerVerlag, 2002.
[16] A. d'Avila Garcez, D. Gabbay, S. Holldobler, and J. Taylor, J. Applied Logic, special issue on neuralsymbolic systems, vol. 2, no. 3, 2004.
[17] A. d'Avila Garcez and L.C. Lamb, "A Connectionist Computational Model for Epistemic and Temporal Reasoning," Neural Computation, vol. 18, no. 7, pp. 17111738, 2006.
[18] A. d'Avila Garcez, L.C. Lamb, and D.M. Gabbay, "Connectionist Computations of Intuitionistic Reasoning," Theoretical Computer Science, vol. 358, no. 1, pp. 3455, 2006.
[19] A. d'Avila Garcez, L.C. Lamb, and D.M. Gabbay, "Connectionist Modal Logic: Representing Modalities in Neural Networks," Theoretical Computer Science, vol. 371, nos. 1/2, pp. 3453, 2007.
[20] A. d'Avila Garcez, L.C. Lamb, and D.M. Gabbay, NeuralSymbolic Cognitive Reasoning. SpringerVerlag, 2008.
[21] A.Z. Ghalwash, "A Recency Inference Engine for Connectionist Knowledge Bases," Applied Intelligence, vol. 9, no. 3, pp. 201215, 1998.
[22] H. Gust, K.U. Kuhngerger, and P. Geibel, "Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory," Perspectives of NeuralSymbolic Integration, vol. 23, pp. 233264, SpringerVerlag, 2007.
[23] Perspectives of NeuralSymbolic Integration, B. Hammer and P. Hitzler, eds. SpringerVerlag, 2007.
[24] I. Hatzilygeroudis and J. Prentzas, "Neurules: Improving the Performance of Symbolic Rules," Int'l J. AI Tools (IJAIT), vol. 9, no. 1, pp. 113130, 2000.
[25] I. Hatzilygeroudis and J. Prentzas, "Constructing Modular Hybrid Knowledge Bases for Expert Systems," Int'l J. AI Tools (IJAIT), vol. 10, nos. 1/2, pp. 87105, 2001.
[26] I. Hatzilygeroudis and J. Prentzas, "An Efficient Hybrid Rule Based Inference Engine with Explanation Capability," Proc. 14th Int'l FLAIRS Conf., pp. 227231, 2001.
[27] I. Hatzilygeroudis and J. Prentzas, "NeuroSymbolic Approaches for Knowledge Representation in Expert Systems," Int'l J. Hybrid Systems (IJHIS), vol. 1, nos. 3/4, pp. 111126, 2004.
[28] I. Hatzilygeroudis and J. Prentzas, "Using a Hybrid RuleBased Approach in Developing an Intelligent Tutoring System with Knowledge Acquisition and Update Capabilities," J. Expert Systems with Applications, vol. 26, no. 4, pp. 477492, 2004.
[29] M. Hilario, "An Overview of Strategies for Neurosymbolic Integration," ConnectionistSymbolic Integration: From Unified to Hybrid Approaches, R. Sun and E. Alexandre, eds., Lawrence Erlbaum, 1997.
[30] M. Hilario and A. Rida, "The Use of Prior Knowledge in Neural Network Configuration and Training," Biological and Artificial Computation: From Neuroscience to Technology, pp. 227236, Springer, 1997.
[31] S. Holldobler and Y. Kalinke, "Towards a Massively Parallel Computational Model for Logic Programming," Proc. ECAI94 Workshop Combining Symbolic and Connectionist Processing (ECCAI '94), pp. 6877, 1994.
[32] S. Holldobler, Y. Kalinke, and H.P. Storr, "Approximating the Semantics of Logic Programs by Recurrent Neural Networks," Applied Intelligence, vol. 11, no. 1, pp. 4558, 1999.
[33] E. Komendantskaya, M. Lane, and A.K. Seda, "Connectionist Representation of MultiValued Logic Programs," Perspectives of NeuralSymbolic Integration, pp. 283313, Springer, 2007.
[34] L.C. Lamb, R.V. Borges, and A. d'Avila Garcez, "A Connectionist Cognitive Model for Temporal Synchronisation and Learning," Proc. 22nd Conf. Artificial Intelligence (AAAI '07), pp. 827832, 2007.
[35] J.J. Mahoney and R. Mooney, "Combining Connectionist and Symbolic Learning to Refine CertaintyFactor Rule Bases," Connection Science, vol. 5, nos. 3/4, pp. 339364, 1993.
[36] K. McGarry, S. Wermter, and J. MacIntyre, "Hybrid Neural Systems: From Simple Coupling to Fully Integrated Neural Networks," Neural Computing Surveys, vol. 2, pp. 6293, 1999.
[37] L.R. Medsker, Hybrid Intelligent Systems, second printing. Kluwer Academic Publishers, 1998.
[38] L. SouiciMeslati and M. Sellami, "Toward a Generalization of NeuroSymbolic Recognition: An Application to Arabic Words," Int'l J. KnowledgeBased and Intelligent Eng. Systems, vol. 10, no. 5, pp. 347361, 2006.
[39] C.W. Omlin and C.L. Giles, "Rule Revision with Recurrent Neural Networks," IEEE Trans. Knowledge and Data Eng., vol. 8, no. 1, pp. 183188, Feb. 1996.
[40] T.R. Payne and P. Edwards, "Implicit Feature Selection with the Value Difference Metric," Proc. 13th European Conf. Artificial Intelligence, Henri Prade, ed., pp. 450454, 1998.
[41] J. Prentzas and I. Hatzilygeroudis, "Construction of Neurules from Training Examples: A Thorough Investigation," Proc. ECAI06 Workshop "NeuralSymbolic Learning and Reasoning" (NeSy '06), A. Garcez, P. Hitzler, and G. Tamburini, eds., pp. 3540, Aug./Sept. 2006.
[42] H. Reichgelt, Knowledge Representation: An AI Perspective. Ablex, 1991.
[43] L. Shastri, "SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference," Perspectives of NeuralSymbolic Integration, pp. 183203, Springer, 2007.
[44] J. Sima, "Neural Expert Systems," Neural Networks, vol. 8, no. 2, pp. 261271, 1995.
[45] J. Sima and J. Cervenka, "Neural Knowledge Processing in Expert Systems," KnowledgeBased Neurocomputing, I. Cloete and J.M. Zurada, eds., pp. 419466, The MIT Press, 2000.
[46] A.H. Tan, "Cascade ARTMAP: Integrating Neural Computation and Symbolic Knowledge Processing," IEEE Trans. Neural Networks, vol. 8, no. 2, pp. 237250, 1997.
[47] T.H. Teng, Z.M. Tan, and A.H. Tan, "SelfOrganizing Neural Models Integrating Rules and Reinforcement Learning," Proc. IEEE Int'l Joint Conf. Neural Networks, pp. 37713778, 2008.
[48] H. Tirri, "Replacing the Pattern Matcher of an Expert System with a Neural Network," Intelligent Hybrid Systems, S. Goonatilake and K. Sukdev, eds., John Wiley & Sons, 1995.
[49] G. Towell and J. Shavlik, "Extracting Refined Rules from KnowledgeBased Neural Networks," Machine Learning, vol. 13, no. 1, pp. 71101, 1993.
[50] G. Towell and J. Shavlik, "KnowledgeBased Artificial Neural Networks," Artificial Intelligence, vol. 70, nos. 1/2, pp. 119165, 1994.
[51] Hybrid Neural Systems, S. Wermter and R. Sun, eds. SpringerVerlag, 2000.
[52] J.C. Xianyu, Z.C. Juan, and L.J. Gao, "KnowledgeBased Neural Networks and Its Application in Discrete Choice Analysis," Proc. Fourth Int'l Conf. Networked Computing and Advanced Information Management, pp. 491496, 2008.
[53] J. Yu, L. Xi, and X. Zhou, "Intelligent Monitoring and Diagnosis of Manufacturing Processes Using an Integrated Approach of KBANN and GA," Computers in Industry, vol. 59, no. 5, pp. 489501, 2008.
[54] L. Yu, L. Wang, and J. Yu, "Identification of Product Definition Patterns in Mass Customization Using a LearningBased Hybrid Approach," Int'l J. Advanced Manufacturing Technologies, vol. 38, nos. 11/12, pp. 10611074, 2008.