Monographs and Textbooks

  1. J. Mańdziuk, (2010), Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, vol. 276 of Studies in Computational Intelligence (J. Kacprzyk Series Editor), Springer-Verlag, 254 pages, ISBN: 978–3-642-11677-3 with forewords by David B. Fogel and Simon M. Lucas

  2. J. Mańdziuk, (2000), Hopfield-type neural networks. Theory and applications, EXIT, ISBN 83-87674-17-6, (in Polish), TOC, preface

  3. M. Borkowski and J. Mańdziuk, (2003), Operating systems, WSEiZ Publishing House, ISBN 83-918469-8-9, (in Polish)

 

Edited Volumes

  1. W. Duch and J. Mańdziuk (Eds.), (2007), Challenges for Computational Intelligence, vol. 63 of Studies in Computational Intelligence (J. Kacprzyk Series Editor), Springer-Verlag, 488 pages, ISBN 978-3-540-71983-0 TOC, preface

  2.  J. Mańdziuk (Ed.), (2002), Special Issue on Neural Networks for Optimization and Control, Control and Cybernetics, vol. 31, no. 2, ISSN 0324-8569, TOC, preface

 

Journal Publications (including Springer's LNCS)

  1. J. Mańdziuk, M. Świechowski, (2012), Generic heuristic approach to General Game Playing, Lecture Notes in Computer Science, vol. 7147, 649-660, Springer-Verlag

  2. M. Kobos, J. Mańdziuk, (2011), Multiple-resolution classification with combination of density estimators, Connection Science, 23(4), 219-237, Taylor & Francis

  3. K. Walędzik, J. Mańdziuk, (2011), Multigame playing by means of UCT enhanced with automatically generated evaluation functions, Lecture Notes in Artificial Intelligence, vol. 6830, 327-332, Springer-Verlag, (long version)

  4. J. Mańdziuk, (2011), Towards Cognitively Plausible Game Playing Systems, IEEE COMPuTATIOnal IntelligenCE MAGAzINE, 6(2), May, 38-51

  5. J. Mańdziuk, M. Jaruszewicz, (2011), Neuro-genetic system for stock index prediction, Journal of Intelligent & Fuzzy Systems, 22(2), 93-123, IOS Press

  6. M. Kobos, J. Mańdziuk, (2010), Classification Based on Multiple-Resolution Data View, Lecture Notes in Computer Science, vol. 6354, 124-129, Springer-Verlag

  7. K. Walędzik, J. Mańdziuk, (2010), The Layered Learning method and its application to generation of evaluation functions for the game of checkers, Lecture Notes in Computer Science, vol. 6239, 543-552, Springer-Verlag

  8. K. Walędzik, J. Mańdziuk, (2010), CI in General Game Playing - to date achievements and perspectives, Lecture Notes in Artificial Intelligence, vol. 6114, 667-674, Springer-Verlag

  9. J. Mańdziuk, M. Jaruszewicz, (2009), “Dead” chromosomes and their elimination in the neuro-genetic stock index prediction system, Lecture Notes in Computer Science, vol. 5864, 601-610, Springer-Verlag

  10. M. Kobos, J. Mańdziuk, (2009), Classification Based on Combination of Kernel Density Estimators, Lecture Notes in Computer Science, vol. 5769, 125-134, Springer-Verlag

  11. C. Dendek, J. Mańdziuk, (2009), Probability-based distance function for distance-based classifiers, Lecture Notes in Computer Science, vol. 5768, 141-150, Springer-Verlag

  12. K. Mossakowski, J. Mańdziuk, (2009), Learning without human expertise. A case study of the Double Dummy Bridge Problem, IEEE Transactions on Neural Networks, 20(2), 278-299

  13. F. Fazayeli, L. Wang, and J. Mańdziuk, (2008), Feature Selection Based on the Rough Set Theory and EM Clustering Algorithm, Lecture Notes in Artificial Intelligence, vol. 5306, 272-282, Springer-Verlag

  14. R. Grodzicki, J. Mańdziuk, L. Wang, (2008), Improved Multilabel Classification with Neural Networks, Lecture Notes in Computer Science, vol. 5199, 409-416, Springer-Verlag

  15. C. Dendek, J. Mańdziuk, (2008), Improving Performance of a Binary Classifier by Training Set Selection, Lecture Notes in Computer Science, vol. 5163, 128-135, Springer-Verlag

  16. M. Kobos and J. Mańdziuk, (2008), Artificial Intelligence methods in stock index prediction with the use of newspaper articles, Foundations of Control and Management Science, no.9, 67-76

  17. J. Mańdziuk,  M. Kusiak, K. Walędzik, (2007), Evolutionary-based heuristic generators for checkers and give-away checkers, ExpErt Systems, 24(4), 189-211, Blackwell-Publishing

  18. P. Kupis, J. Mańdziuk, (2007), Multiple sequence alignment with evolutionary-progressive method, Lecture Notes in Computer Science, vol. 4431, 23-30, Springer-Verlag

  19.  M. Kusiak, K. Walędzik, J. Mańdziuk, (2007), Evolutionary approach to the game of checkers, Lecture Notes in Computer Science, vol. 4431, 432-440, Springer-Verlag

  20. C. Dendek, J. Mańdziuk, (2006), Including Metric Space Topology in Neural Network Training by Ordering Patterns, Lecture Notes in Computer Science, vol. 4132, 644-653, Springer-Verlag

  21. K. Mossakowski and J. Mańdziuk (2006), Neural networks and the estimation of hands' strength in contract bridge, Lecture Notes in Artificial Intelligence, vol. 4029, 1189-1198, Springer-Verlag

  22. M. Kusiak, K. Walędzik and J. Mańdziuk (2005), Evolution of heuristics for give-away checkers, Lecture Notes in Computer Science, vol. 3697, 981-987, Springer-Verlag

  23. N.H. Viet and J. Mańdziuk (2005), Neural and Fuzzy Neural Networks in Prediction of Natural Gas Consumption, Neural, Parallel & Scientific Computations, 13(3-4): 265-286

  24. D. Osman and J. Mańdziuk (2004), Comparison of TDLeaf(λ) and TD(λ) learning in game playing domain, Lecture Notes in Computer Science, vol. 3316, 549-554, Springer-Verlag

  25. J. Mańdziuk and D. Osman (2004), Alpha-beta search enhancements with a real-value game state evaluation function, ICGA Journal, 27(1), 38-43

  26. M. Jaruszewicz and J. Mańdziuk (2004), One day prediction of NIKKEI index considering information from other stock markets, Lecture Notes in Artificial Intelligence, vol. 3070, 1130-1135, Springer-Verlag

  27. K. Mossakowski and J. Mańdziuk (2004), Artificial neural networks for solving Double Dummy Bridge problems, Lecture Notes in Artificial Intelligence, vol. 3070, 915-921, Springer-Verlag

  28. J. Mańdziuk and D. Osman (2004), Temporal Difference approach to playing Give-Away Checkers, Lecture Notes in Artificial Intelligence, vol. 3070, 909-914, Springer-Verlag

  29. J. Mańdziuk (2002), Neural networks for the n-queens problem: a review, Control and Cybernetics, 31(2), 217-248

  30. J. Mańdziuk and R. Mikołajczak (2002), Chaotic time series prediction with feed-forward and recurrent neural nets, Control and Cybernetics, 31(2), 383-406

  31. J. Mańdziuk and L. Shastri (2002), Incremental Class Learning approach and its application to Handwritten Digit Recognition, Information Sciences, 141(3-4): 193-217

  32. J. Mańdziuk (2000), Optimization with the Hopfield network based on correlated noises: an empirical approach, Neurocomputing, 30(1-4): 301-321

  33. J. Mańdziuk (1999), Improvement of the Hopfield Associative Memory by contour enhancement in library patterns, Neural, Parallel & Scientific Computations, 7(3): 359-378, (invited paper)

  34. A. Jagota and J. Mańdziuk (1998), Experimental study of Perceptron-type local learning rule for Hopfield associative memory, Information Sciences, 111 (1-4): 65-81

  35. J. Mańdziuk (1996), Solving the Travelling Salesman Problem with the Hopfield-type neural network, Demonstratio Mathematica, 29(1): 219-231

  36. J. Mańdziuk, C. Gorecki and B. Macukow (1996), Cross Absolute Filter for removing speckle noise from interference patterns, Optical Review,  3(4): 269-275

  37. J. Mańdziuk (1995), Solving the N-Queens Problem with a binary Hopfield-type network. Synchronous and asynchronous model, Biological Cybernetics, 72(5): 439-446

  38. J. Mańdziuk and B. Macukow (1993), A neural network performing Boolean logic operations, Optical Memory and Neural Networks, 2 (1): 17-35

  39. J. Mańdziuk (1993), An Isomorphism Theorem for Unicyclic Graphs, Demonstratio Mathematica, 26 (2): 253-264

  40. J. Mańdziuk and B. Macukow (1992), A neural network designed to solve the N-Queens Problem, Biological Cybernetics, 66 (4): 375-379

 

Book Chapters

  1. M. Kobos and J. Mańdziuk, (2008), Metody sztucznej inteligencji w przewidywaniu wartości indeksu giełdowego z wykorzystaniem artykułów prasowych, In: C. Orłowski, Z. Kowalczuk, E. Szczerbicki (eds), Zarządzanie Wiedzą i Technologiami Informatycznymi, PWNT Gdańsk, 61-68, (in Polish)

  2. J. Mańdziuk, (2007), Computational Intelligence in Mind Games, (abstract), In: W. Duch and J Mańdziuk (Eds.), Challenges for Computational Intelligence, Studies in Computational Intelligence, vol. 63, 407-442, Springer-Verlag, ISBN 978-3-540-71983-0

  3. M. Jaruszewicz and J. Mańdziuk, (2006), Neuro-genetic system for DAX index prediction. In: A. Cader et al. (Eds.) Artificial Intelligence and Soft Computing, 42-49, EXIT, ISBN 83-87674-90-7

  4. D. Osman and J. Mańdziuk, (2005), TD-GAC: Machine Learning Experiment with Give-Away Checkers, In: M. Dramiński et al. (Eds.) Issues in Intelligent Systems. Models and Techniques, 131-145, EXIT, ISBN 83-87674-91-5

  5. K. Mossakowski and J. Mańdziuk, (2005), Weight patterns in the networks trained to solve double-dummy bridge problem, In: O. Hryniewicz et al. (Eds.) Issues in Intelligent Systems. Paradigms, 155-165, EXIT, ISBN 83-87674-90-7 

  6. W. Duch and J. Mańdziuk, (2004), Quo vadis, computational intelligence ?, In: Machine Intelligence: Quo Vadis? The Progressive Trends in Intelligent Technologies, In: P. Sincak, J. Vascak and K. Hirota (Eds.) Advances in Fuzzy Systems - Applications and Theory, vol. 21, 3-28, World Scientific, ISBN 981-238-751-X

 

Book Reviews

  1. J. Mańdziuk, (2007), Book review: Neural Networks and Sea Time Series.  Reconstruction and Extreme-Event Analysis by B. Tirozzi, S. Puca, S. Pittalis, A. Bruschi, S. Morucci, E. Ferraro and S. Corsini (Eds.). (Birkhäuser, Boston, 2005, in N. Bellomo (Series Editor), Modeling and Simulation in Science, Engineering and Technology, 179 pages, hardbound, ISBN 10-8176-4347-8. Published in: Control and Cybernetics, vol. 36, no. 4, 1051-1053

  2. J. Mańdziuk (2005), Book review: Computational Intelligence in Games by N. Baba and L. Jain (Eds.). (Physica-Verlag, Springer, Heidelberg, New York, 2001, Studies in Fuzziness and Soft Computing, J. Kacprzyk (Ed.), vol. 62, 161 pages, hardbound, ISBN 3-7908-1348-6). Published in: IEEE Transactions on Neural Networks, 16(2), 509-510

  3. J. Mańdziuk (2002), Book review: Advanced Mean Field Methods: Theory and Practice by M. Opper and D. Saad (Eds.). (The MIT Press, Cambridge, Massachusetts, London, England, 2001, Neural Information Processing Series, 273 pages, hardbound, ISBN 0-262-15054-9). Published in: IEEE Transactions on Neural Networks, 13(3), 785-786

 

International Conference Papers

  1. J. Mańdziuk, K. Mossakowski, (2009), Neural networks compete with expert human players in solving the Double Dummy Bridge Problem, Computational Intelligence and Games (CIG’09), 117-124, Mediolan, Włochy, IEEE Press

  2. C. Dendek, J. Mańdziuk, (2008), A Neural Network Classifier of Chess Moves, 8th Hybrid Intelligent Systems Conference (HIS 2008), Barcelona, Spain,  338-343, IEEE Press

  3. J. Mańdziuk, (2008), Some thoughts on using Computational Intelligence methods in classical mind board games, International Joint Conference on Neural Networks (IJCNN'2008), Hong Kong, 4001-4007, IEEE Press

  4. J. Mańdziuk, M. Jaruszewicz, (2007), Neuro-evolutionary approach to stock market prediction, International Joint Conference on Neural Networks (IJCNN'07), Orlando, FL, USA, 2515-2520, IEEE Press

  5. P. Kupis, J. Mańdziuk, (2007), Evolutionary-progressive method for multiple sequence alignment, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’07), Honolulu, Hawaii, USA, 291-297, IEEE Press

  6. J. Mańdziuk, K. Mossakowski (2007), Example-based estimation of hand’s strength in the game of bridge with or without using explicit human knowledge, IEEE Symposium on Computational Intelligence in Data Mining (CIDM’07), Honolulu, Hawaii, USA, 413-420, IEEE Press

  7. J. Mańdziuk and K. Mossakowski (2004), Looking inside neural networks trained to solve double-dummy bridge problems, 5th Game-On International Conference on Computer Games: Artificial Intelligence, Design and Education (CGAIDE’ 2004), Reading, UK, 182-186

  8. N. H. Viet and J. Mańdziuk (2003), Neural and fuzzy neural networks for natural gas consumption prediction, IEEE International Workshop on Neural Networks for Signal Processing (NNSP’03), Toulouse, France, 759-768

  9. N. H. Viet and J. Mańdziuk (2003), Prediction of natural gas consumption with feed-forward and fuzzy neural networks, 6th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’03), Roanne, France, Springer-Verlag, Wien, 107-114

  10. M. Jaruszewicz and J. Mańdziuk (2003), Short-term weather forecasting with neural networks, In: Advances in Soft Computing (L. Rutkowski and J. Kacprzyk, eds.), Physica-Verlag (Springer), 843-848

  11. M. Jaruszewicz and J. Mańdziuk (2002), Application of PCA method to weather prediction task, 9th International Conference on Neural Information Processing (ICONIP’02), Singapore, vol. 5, 2359-2363

  12. R. Mikołajczak and J. Mańdziuk (2002), Comparative study of logistic map series prediction using feed-forward, partially recurrent and general regression networks, 9th International Conference on Neural Information Processing (ICONIP’02), Singapore, vol. 5, 2364-2368 

  13. R. Mikołajczak and J. Mańdziuk (2002), Chaotic time series prediction with neural networks - comparison of several architectures, 15th European Conference on Artificial Intelligence (ECAI’2002), Lyon, France, 493-497

  14. J. Mańdziuk (2000), Incremental Training in Game Playing Domain, International ICSC Congress on Intelligent Systems & Applications (ISA’2000), International Symposium on Computational Intelligence (CI’2000), Wollongong, Australia, vol. 2: 18-23

  15. J. Mańdziuk (2000), Incremental learning approach for board game playing agents,  The 2000 International Conference on Artificial Intelligence (IC-AI’2000), Las Vegas, USA, vol. 2: 705-711

  16. J. Mańdziuk and L. Shastri (1999), Incremental Class Learning – an approach to longlife and scalable learning, International Joint Conference on Neural Networks (IJCNN’99), Washington D.C., USA, (6 pages, distributed on CD)

  17. J. Mańdziuk and L. Shastri (1999), On some issues in Incremental Class Learning, 4th Conference on Neural Networks and Their Applications, Zakopane, 89-97

  18. J. Mańdziuk (1998), Improvement of the Hopfield Associative Memory by contour enhancement, International Conference on Neural Networks and Brain (ICNN&B’98), Beijing, China, 90-93

  19. J. Mańdziuk and L. Shastri (1998), Incremental Class Learning approach and its application to Handwritten Digit Recognition, 5th International Conference on Neural Information Processing (ICONIP’98), Kitakyushu, Japan, vol. 1: 203-206

  20. J. Mańdziuk (1997), Pulsed noise - based stochastic optimization with the Hopfield model, IEEE International Conference on Neural Networks  (ICNN'97), Houston, USA, vol. 2: 1315-1320

  21. J. Mańdziuk and A. Jagota (1997), Experimental study of Perceptron-type Online Local Learning Rule for Hopfield Associative Memory, 2nd International Conference on Computational Intelligence and Neuroscience (ICCIN'97), Research Triangle Park, USA, vol. 2: 168-172

  22. J. Mańdziuk (1997), Non-delta-correlated gaussian noises and the Hopfield optimisation circuit: an empirical study, 2nd International Conference on Computational Intelligence and Neuroscience (ICCIN'97), Research Triangle Park, USA, vol. 2: 110-113

  23. J. Mańdziuk (1996), Improving performance of the bipolar Hopfield network by supervised learning, World Congress on Neural Networks (WCNN'96), San Diego, USA, 267-270

  24. C. Gorecki and J. Mańdziuk (1995), Speckle noise removal in interference patterns by non-linear techniques: comparison with median filters, SPIE vol. 2544 - Interferometry VII: Techniques and Analysis, SPIE's 40th Annual Meeting, San Diego, USA, 130-137

  25. J. Mańdziuk and B. Macukow (1993), Neural Networks, Recognition Based on Differences, ICO-16 Congress on "Optics as a Key to High Technology", Budapest, Hungary, SPIE vol. 1983, 470-471

  26. J. Mańdziuk and B. Macukow (1992), Neural-Network Model of Content-Addressable Memory, International Conference on Modeling Problems in Bionics (BIOMOD'92), St. Petersburg, Russia, 319-320

 

Other Publications

  1. J. Mańdziuk (2009), Technologie informatyczne: Sharepoint zastąpi dotychczasowy intranet, Gazzeta, 2(48), 4-5, (in Polish)

  2. J. Mańdziuk (1999), Y2K nadchodzi, Nasza gazownia, 2: 20, (in Polish)

  3. J. Mańdziuk and L. Shastri (1998), Incremental Class Learning approach and its application to Handwritten Digit Recognition, Technical Report TR-98-015, International Computer Science Institute, Berkeley, USA

  4. J. Mańdziuk (1997), Optimization with the Hopfield network based on correlated noises: an empirical approach, Technical Report TR-97-019, International Computer Science Institute, Berkeley, USA

  5. P. Duda, P. Figurny and J. Mańdziuk (1993), System of data acquisition and visualization for digital multimeter Keithley , Institute of High Pressure Physics of the Polish Academy of Science, (in Polish) – (industrial implementation)

  6. J. Mańdziuk (1993), An Isomorphism Theorem for Unicyclic Graphs, Preprint of the Institute of Mathematics, Warsaw University of Technology

  7. J. Mańdziuk (1993), Application of neural networks to solving optimization problems, Faculty of Technical Physics and Applied Mathematics, Warsaw University of Technology, Ph.D. Thesis, (in Polish)