BIP WSIiZ

Prof. dr hab. Janusz Starzyk

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Kierownik Katedry Zastosowań Systemów Informatycznych.

Profesor w Katedrze Zastosowań Systemów Informatycznych.

Profesor honorowy WSIiZ.

Zatrudniony w Wyższej Szkole Informatyki i Zarządzania w Rzeszowie od 2007 roku.

Doktor habilitowany w dziedzinie elektrotechniki  (Politechnika Śląska w Gliwicach), doktor nauk w dziedzinie elektrotechniki (Politechnika Warszawska), magister matematyki stosowanej (Politechnika Warszawska).

Pracował jako adiunkt w Instytucie Podstaw Elektroniki na Politechnice Warszawskiej. Następnie odbył dwuletni staż jako post-doc i badacz na Uniwersytecie McMaster w Hamilton, Kanada. Od roku 1991 jest profesorem na Wydziale Elektrotechniki i Informatyki w Uniwersytecie Ohio w Athens, USA, oraz dyrektorem Laboratorium Embodied Intelligence. Od roku 2009 jest kierownikiem Kierownik Katedry Zastosowań Systemów Informatycznych WSIZ.

Współpracował z National Institute of Standards and Technology, USA w obszarze testowania i diagnostyki układów analogowych i mieszanych. Był profesorem wizytującym na University of Florence, Włochy oraz w Nanyang Technological University, Singapur. Był konsultantem Magnetek Corp. oraz doradcą technicznym i starszym badaczem w Magnolia Broadband Inc. Włochy. Przez kilka lat był naukowcem wizytującym w Wright Labs – Advanced Systems Research Group oraz Redstone Arsenal – U.S. Army Test, Measurement, and Diagnostic Activity. Przez rok był na stanowisku IPA fellow at Wright Research Labs, Automatic Target Recognition Group. Był wizytującym badaczem w ATT Bell Laboratories – VLSI Systems Research Group and Sarnoff Research Labs. – Mixed Signal VLSI Design Group.

Badania

Obecne badania obejmują ucieleśnioną inteligencję maszyn, samoorganizujące celowe uczenie, motywowane mechanizmy interakcji sensoryczno-motorycznych, asocjacyjne pamięci przestrzenno-czasowe, pamięci epizodyczne jak również stosowanie uczenia maszynowego do inteligentnego sterowania robotów i agentów wirtualnych, budowa środowisk wirtualnych, analiza wzorców, drążenie danych i rynki finansowe.

Uczenie motywowane robotów i awatarów w środowiskach wirtualnych

Badania koncentrują się na projektowaniu i rozwoju systemów obliczeniowych, które uczą się, działają i komunikują w zmieniającym się, nieprzewidywalnym środowisku poprzez wykształcenie wysokopoziomowych reprezentacji sensorycznych środowiska i rozwój ich własnych możliwości motorycznych. Celem jest zaprojektowanie zainspirowanego biologią, samoorganizującego, uczenia motywowanego (motivated learning – ML) oraz narzędzi rozwiązujących problemy w celu zbudowania wydajnych systemów mogących formułować i osiągać złożone cele wykorzystując ograniczone zasoby obliczeniowe.

Uczenie motywowane będzie rozwijało agentów kognitywnych, którzy będą analizować i działać według dostępnych informacji, poszukiwać dalszych informacji i realizować określone zgodnie z własnymi, wewnętrznie opracowanymi wzorcami zachowań. Celem jest lepsze zrozumienie roli interakcji ze środowiskiem dla percepcji, poznania i interakcji. Rozwój ten opiera się na mechanizmie uczenia motywowanego, które stosuje samoorganizację, tworzenie celów i uczenie się zorientowane na cele (włączając hierarchiczne uczenie ze wzmocnieniem i uczenie oparte o ciekawość). Badania są skoncentrowane na teoretycznych aspektach rozwoju motywowanych systemów kognitywnych oraz ich praktycznych zastosowaniach w robotach i agentach wirtualnych.

Ważnym pytaniem jest jak zmotywować agenta do czegokolwiek, oraz jak ogólnie zwiększyć jego własną złożoność? Odpowiedź na to pytanie może być uzyskana przez wprowadzenie uczenia motywowanego. ML sprawia, że agent EI (Inteligencji ucieleśnionej -Embodied Intelligence) wyznacza swoje własne cele i rozwija wewnętrzny system nagród. Dostarczając agentowi wewnętrzna potrzebę uczenia, ustalania własnych celów, wyznaczania stopnia powodzenia jego działań, uczenie motywowane może prowadzić do inteligentnych zachowań. Agent ML otrzymuje nagrodę ze środowiska za realizacje swoich najbardziej podstawowych celów, jednakże nie cierpi on na szybki wzrost wysiłku uczenia w dynamicznym złożonym środowisku. Stosuje system kreowania celu (Goal Creation System GCS) aby określać i zarządzać celami abstrakcyjnymi. ML ulepsza możliwości agenta do postrzegania użytecznych obiektów i uczenia użytecznych umiejętności motorycznych.

Strona bieżącego projektu „Budowa efektywnych mechanizmów percepcji robota z zastosowaniem uczenia motywowanego oraz samoorganizującej pamięci asocjacyjnej” : perception.wsiz.edu.pl

Strona projektu „Organizacja pamięci semantycznej i epizodycznej w uczeniu motywowanym robotów”: ncn.wsiz.edu.pl

 

http://orcid.org/0000-0003-2678-5515

Niektóre nowe publikacje znajdują się na stronie projektu „Budowa efektywnych mechanizmów percepcji robota z zastosowaniem uczenia motywowanego oraz samoorganizującej pamięci asocjacyjnej”  : perception.wsiz.edu.pl

Rozdziały w książkach

  1. W. Galus, J. A. Starzyk, Świadomość? Ależ to bardzo proste! BEL Studio, Warszawa, 1, 2018. 372 pp.

  2. M. Jaszuk, J. A. Starzyk, “Building Internal Scene Representation in Cognitive Agents”, in Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions, series: Advances in Intelligent Systems and Computing, 364, Springer, pp 479-491, 2016.

  3. J. A. Starzyk and Basawaraj, “Comparison of two memristor based neural network learning schemes for crossbar architecture”, Advances in Computational Intelligence, Lecture Notes in Computer Science, vol. 7902, pp. 492-499, 2013.

  4. J. A. Starzyk, J. Graham, and L. Puzio, “Simulation of a Motivated Learning Agent”, Artificial Intelligence Applications and Innovations , Vol. 412, IFIP Advances in Information and Communication Technology, pp. 205-214, 2013.

  5. J. A. Starzyk, Motivated Learning for Computational Intelligence, in Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, edited by B. Igelnik, IGI Publishing, ch.11, pp. 265-292, 2011.

  6. J. A. Starzyk, Y. Liu, S. Batóg, “A Novel Optimization Algorithm Based on Reinforcement Learning”, in `Computational Intelligence in Optimization-Applications and Implementations’, Tenne, Yoel; Goh, Chi-Keong (Eds.), Springer Verlag, 2010, pp. 27-49.

  7. „Introduction to Computer Design and Analysis of Electronic Networks”, (Co‑author), Wydawnictwa PW, Warszawa, 1978 (in Polish).

  8. Co‑author of Polish translation „Computer‑aided analysis of electronic circuits, algorithms and computational techniques”, by L.O. Chua and P.M. Lin, Wydawnictwa Naukowo‑Techniczne, Warszawa, 1981

  9. „Advances in Circuits and Systems – Selected Papers on Analog Fault Diagnosis”, (co-author), IEEE Press, New York 1987.

  10. „Analog Methods for Circuit Analysis and Diagnosis”, (Co‑author), Marcel Dekker, Inc., New York, 1988.

  11. J. A. Starzyk, “Topological Analysis and Diagnosis of Analog Circuits”, Wydawnictwa Politechniki Slaskiej, 2008, 140 pp.

  12. J. A. Starzyk, „Motivation in Embodied Intelligence” in Frontiers in Robotics, Automation and Control, I-Tech Education and Publishing, Austria, 2008, pp. 83-110

 

Artykuły w czasopismach

  1. Basawaraj, J. A. Starzyk, and A. Horzyk “Episodic Memory in a Minicolumn Associative Knowledge Graph”, submitted to IEEE Trans on Neural networks and Learning Systems.

  2. J. A. Starzyk, Ł. Maciura, and A. Horzyk, „Associative Memories with Synaptic Delays” accepted to IEEE Trans on Neural networks and Learning Systems.

  3. A. Horzyk, J. A. Starzyk, J. Graham, “Integration of Semantic and Episodic Memories”, IEEE Trans on Neural networks and Learning Systems, vol. 28, no. 12, Dec. 2017, pp. 3084-3095, DOI: 10.1109/TNNLS.2017.2728203.

  4. J. A. Starzyk, J. Graham, L. Puzio, “Needs, Pains, and Motivations in a Simulated Learning Agent,” IEEE Trans on Neural networks and Learning Systems, vol. 28, no.11, Nov. 2017, pp. 2528-2540.

  5. J. A. Starzyk, J. Graham, “MLECOG – Motivated Learning Embodied Cognitive Architecture” IEEE Systems Journal, vol. 11, no. 3, 2017, pp. 1272-1283.

  6. T.-H. Teng, A.-H. Tan, J. A. Starzyk, “Multi-Agent Reinforcement Learning with Task Dependencies and Resource Competition”, submitted to IEEE Trans on Neural Networks and Learning Systems, 2015.

  7. J. Graham, J. A. Starzyk, and D. Jachyra, “Opportunistic Behavior in Motivated Learning Agents”, IEEE Trans on Neural networks and Learning Systems, vol. 26, no. 8, Aug. 2015, pp. 1735-1746.

  8. J. A. Starzyk, and Basawaraj, “Memristor Crossbar Architecture for Synchronous Neural Networks”, IEEE Trans. Circuits and Systems, Part I, March 2014, pp.1-12.

  9. V. A. Nguyen, J. A. Starzyk and W-B. Goh,”A Spatio-temporal Long-term Memory Approach for Visual Place Recognition in Mobile Robotic Navigation” –Robotics and Autonomous Systems, Elsevier, vol. 61, no. 12, Dec. 2013, pp. 1744–1758.

  10. W. Wang, B. Subagdja, A.-H. Tan, and J. A. Starzyk, “Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting” IEEE Trans on Neural Networks and Learning Systems, vol. 23, no. 10, Oct. 2012, pp. 1574 – 1586.

  11. T. Huang, C. Li, S. Duan, and J. A. Starzyk, „Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects”  IEEE Trans on Neural Networks and Learning Systems, vol. 23 , no. 6 , June 2012, pp.  866 – 875.<

  12. V. A. Nguyen, J. A. Starzyk, W-B. Goh, D. Jachyra, “Neural Network Structure for Spatio-Temporal Long-Term Memory” IEEE Trans on Neural Networks and Learning Systems, vol. 23 no. 6, June, 2012, pp. 971-983.

  13. J. A. Starzyk, J. T. Graham, P. Raif, and A-H.Tan, “Motivated Learning for Autonomous Robots Development”, Cognitive Science Research, v.14, no.1, 2012, p.10(16) pp. 10-25.

  14. P. Moghadam, J. A. Starzyk, and W. S. Wijesoma, „Fast Vanishing Point Detection in Unstructured Environments” IEEE Trans. on Image Processing, vol. 21, no. 1, Jan 2012, pp. 425-430.

  15. J. A. Starzyk and D. Prasad, “A Computational Model of Machine Consciousness” International Journal of Machine Consciousness, vol. 3, No. 2 (2011) pp. 255-281.

  16. J. Feng, W-H. Qiu, J. Starzyk, “Risk Assessment of Credit Card Application Based on Self-organizing Learning Array (SOLAR)”, Industrial Engineering Journal, vol. 13, no. 6, pp. 71-75. Nov. 2010

  17. J. A. Starzyk, H. He, “Spatio-Temporal Memories for Machine Learning: A Long-Term Memory Organization”, IEEE Trans. on Neural Networks, vol. 20, no. 5, May 2009, pp 768 – 780.

  18. H. He, X. Shen, J. A. Starzyk, ”Power Quality Disturbances Analysis Based on EDMRA Method”, accepted for Int. Journal of Electrical Power & Energy Systems, vol. 31, no.5, May 2009.

  19. Y. Liu, J. A. Starzyk, Z. Zhu, “Optimized Approximation Algorithm in Neural Network without Overfitting”, IEEE Trans. on Neural Networks, vol. 19, no. 4, June, 2008, pp. 983-995.

  20. H. F. A. Hamed, S. Kaya, J. A. Starzyk, “Use of nano-scale double-gate MOSFETs in low-power tunable current mode analog circuits” Analog Integrated Circuits and Signal Processing, Feb., 2008.

  21. J. A. Starzyk, and H. He, “Anticipation-Based Temporal Sequences Learning in Hierarchical Structure”, IEEE Trans. on Neural Networks, vol. 18,  no. 2,  March 2007, pp. 344 – 358. Received the best research paper award in the College of Engineering and Technology at Ohio University.

  22. J. A. Starzyk and H. He, “A Novel Low Power Logic Circuit Design Scheme,” IEEE Trans. Circuits Syst. II, vol. 54, no. 2, pp.176-180, Feb. 2007.

  23. Z. Zhu, F. van Graas and J. A. Starzyk, “GPS signal acquisition using the repeatability of successive code phase measurements” GPS Solutions, Springer, May 2007.

  24. S. Kaya, H. F. A. Hamed and J. A. Starzyk, “Low-Power Tunable Analog Circuit Blocks Based on Nanoscale Double-Gate MOSFETs,” IEEE Trans. Circuits Syst. II, vol. 54, no. 7, July 2007, pp. 571-575.

  25. J. A. Starzyk, H. He, and Y. Li, “A Hierarchical Self-organizing Associative Memory for Machine Learning”, Lecture Notes in Computer Science 4491: pp. 413-423, 2007.

  26. J. A. Starzyk, Y. Liu, D. Vogel, ”Sparse Coding in Sparse Winner Networks”, Lecture Notes in Computer Science 4492: pp. 534-541, 2007.

  27. Z. Zhu, H. He, J.A. Starzyk, and C. Tseng “Self-Organizing Learning Array and its Application to Economic and Financial Problems” Elsevier Science, Information Sciences, vol. 177, no 5, 1 March 2007, Pages 1180-1192.

  28. J. A. Starzyk, M. Ding, Y. Liu, ”Hybrid Pipeline Structure for Self-Organizing Learning Array”, Lecture Notes in Computer Science, 2007.

  29. H. He, and J. A. Starzyk, “Online Dynamic Value System for Machine Learning”, Lecture Notes in Computer Science 4491: pp. 441-448, 2007.

  30. J. A. Starzyk, Z. Zhu, and Y. Li, „Associative Learning in Hierarchical Self Organizing Learning Arrays“, IEEE Trans. Neural Networks, vol.17, no. 6, pp.1460-1470, Nov. 2006.

  31. H. He and J. A. Starzyk, „A Self Organizing Learning Array System for Power Quality Classification based on Wavelet Transform„, IEEE Trans. on Power Delivery, Jan. 2006.

  32. J. A. Starzyk, Z. Zhu and T.-H. Liu „Self-Organizing Learning Array” IEEE Trans. on Neural Networks, vol. 16, no. 2, pp. 355-363, March 2005.  

  33. J. A. Starzyk, Z. Zhu, and Y. Li, „Associative Learning in Hierarchical Self Organizing Learning Arrays„, Artificial Neural Networks: Biological Inspirations. Lecture Notes in Computer Science 3696: pp. 91-96, 2005.

  34. Janusz A. Starzyk, and Yue Li, David D. Vogel, „Neural Network with Memory and Cognitive Functions„, Artificial Neural Networks: Biological Inspirations. Lecture Notes in Computer Science 3696: pp. 85-90, 2005.

  35. J. A. Starzyk, Dong Liu, Zhi-Hong Liu, D. Nelson, and J. Rutkowski, “Entropy-based optimum test points selection for analog fault dictionary techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 3, June 2004, pp. 754-761.

  36. J. A. Starzyk and F. Wang, „Dynamic Probability Estimator for Machine Learning” IEEE Trans. on Neural Networks, vol.15, no 2, March 2004, pp.298-308.  

  37. J. A. Starzyk, R. P. Mohn, and L. Jing, L., „A Cost-Effective Approach to the Design and Layout of a 14-b Current-Steering DAC Macrocell„, IEEE Trans. on Circuits and Systems I: Fundamental Theory and Applications, Vol. 51 ,  no. 1, Jan. 2004, pp. 196 – 200.

  38. D. E. Nelson, J. A. Starzyk, and D. D. Ensley, „Iterated wavelet transformation and signal discrimination for HRR radar target recognition„,  IEEE Trans. on Systems, Man and Cybernetics, Part A  ,Vol. 33 , no.1 , Jan. 2003 , pp. 52 – 57.

  39. D. Liu and J. A. Starzyk, ” A generalized fault diagnosis in dynamic analog circuits” Int. Journal of Circuit Theory and Applications, vol. 30, 2002, pp. 487-510.

  40. D. E. Nelson, J. A. Starzyk, and D. D. Ensley, „Iterative Wavelet Transformation and Signal Discrimination for HRR Radar Target Recognition,” Multidimensional Systems and Signal Processing, Vol. 14, no.2. 2002.

  41. J. Becker, A. Alsolaim, M. Glesner, and J. Starzyk, “A Parallel Dynamically Reconfigurable Architecture for Flexible Aplication-Tailored Hardware/Software Systems in Future Mobile Communication”, The Journal of Supercomputing, Erratum Vol. 23, 132, 2002,  19(1): 105-127 (2001).

  42. J. Pang and J. A. Starzyk, „Fault Diagnosis in Mixed-Signal Low Testability System” An Int. Journal of Analog Integrated Circuits and Signal Processing, vol. 28, no.2, August 2001, pp. 159-170.

  43. J. A. Starzyk and Y.-W. Jan, and F. Qiu, „A DC-DC Charge Pump Based on Voltage Doublers„, IEEE Trans. Circuits and Systems, Part I, vol. 48, no. 3, March 2001, pp. 350-359.

  44. G. N. Stenbakken, D. Liu J. A. Starzyk, and B. C. Waltrip, „Nonrandom Quatization Errors in Timebases„, IEEE Trans. on Instrumentation and Measurement, vol. 50, no. 4, Aug. 2001, pp.888-892.

  45. J. A. Starzyk, D. E. Nelson, and K. Sturtz, ” A Mathematical Foundation for Improved Reduct Generation in Information Systems„, Journal of Knowledge and Information Systems, v. 2 n. 2, March 2000 p.131-146.

  46. J. A. Starzyk, J. Pang, S. Manetti, G. Fedi, and C. Piccirilli, „Finding Ambiguity Groups in Low Testability Analog Circuits„, IEEE Trans. Circuits and Systems, Part I, vol 47, no. 8, 2000, pp. 1125-1137.

  47. G. Fedi, S. Manetti, J. A. Starzyk, M. C. Piccirilli „Determination of an Optimum Set of Testable Components in the Fault Diagnosis of Analog Circuits„, IEEE Trans. Circuits and Systems, Part I, vol. 46, no.7, 1999, 779-787.

  48. J. A. Starzyk, D. E. Nelson, and K. Sturtz, „Reduct Generation in Information Systems„, Bulletin of International Rough Set Society, 1999, 3 (1/2).

  49. J. A. Starzyk, „Hierarchical Analysis of High Frequency Interconnect Networks„, IEEE Trans. on Computer Aided Design of Integrated Circuits and Systems, vol.13, no.5, 1994, pp. 658-664.

  50. J. A. Starzyk and X. Fang, „A CMOS Current Mode Winner-Take-All Circuit with both Excitatory and Inhibitory Feedback„, Electronics Letters, 1993.

  51. G. N. Stenbakken and J. A. Starzyk, „Diakoptic and Large Change Sensitivity Analysis„, IEE Proc. G, Circuits, Devices and Systems, vol. 139, no.1, 1992, pp.114-118.

  52. J. A. Starzyk and H. Dai, „A Decomposition Approach for Testing Large Analog Networks,” Journal of Electronic Testing – Theory and Applications, no.3, 1992, pp. 181-195.

  53. J. A. Starzyk and A. Konczykowska, „Flowgraph Analysis of Large Electronic Networks„, IEEE Trans. on Circuits and Systems, vol. CAS-33, 1986.

  54. J. A. Starzyk and E. Sliwa, „Upward Topological Analysis of Large Circuits Using Directed Graph Representation„, IEEE Trans. on Circuits and Systems, vol. CAS-31, 1984, pp. 410-414.

  55. E. Salama, J. A. Starzyk and J. W. Bandler, „A Unified Decomposition Approach for Fault Location in Large Analog Circuits„, IEEE Trans. on Circuits and Systems, vol. CAS-31, 1984, pp. 609-622.

  56. J. A. Starzyk, R. M. Biernacki and J. W. Bandler, „Evaluation of Faulty Elements within Linear Subnetworks„, Int. Journal of Circuit Theory and Applications, vol. 12, 1984, pp. 23-37.

  57. J. A. Starzyk and J. W. Bandler, „Multiport Approach to Multiple-Fault Location in Analog Circuits„, IEEE Trans. on Circuits and Systems, vol. CAS-30, 1983, pp. 762-765.

  58. J. A. Starzyk, „An Efficient Cluster Algorithm”, Acta Polytechnica, CVUT, Praha, 1981, pp. 49-55.

  59. J. A. Starzyk, „Signal Flow-Graph Analysis by Decomposition Method„, IEE Proc. on Electronic Circuits and Systems, No. 2, April 1980, pp. 81-86.

  60. G. Centkowski and J. A. Starzyk, „Topological Synthesis of LLF Networks”, Acta Polytechnica, CVUT, Praha, 1980, pp. 77-86.

  61. J. A. Starzyk and E. Sliwa, „Hierarchic Decomposition Method for the Topological Analysis of Electronic Networks„, Int. Journal of Circuit Theory and Applications, Vol. 8, 1980, pp. 407-417.

  62. J. A. Starzyk, „Generation of Complete Trees by the Method of Modified Structural Matrix”, Arch. Elektrot., z.4, 1978, (in Polish), pp. 843-852.

  63. J. A. Starzyk, „New Method for Designing Complete Trees of a Pair of Conjugate Graphs”, Arch. Elektrot., z.1, 1977, (in Polish), pp. 41-46.

  64. J. A. Starzyk, „Application of the Controlled Expansions Method to the Topological Analysis of Circuits”, Arch. Elektrot., z.1, 1977, (in Polish), pp. 47-58.

  65. J. A. Starzyk, „Determination of the Nullator-Norator Graph’s Complete Trees”, Radio Electronics and Communication Systems, t.XX 12, 1977, (in Russian), pp. 9-15.

  66. J. A. Starzyk, and J. Wojciechowski, „Topological Analysis and Synthesis of Electrical Networks by the Method of Structural Numbers”, Raport Naukowy IPE, Warszawa, 1977, (in Polish).

  67. J. A. Starzyk, „Topological Synthesis of Linear Active Networks Described by Multivariable Functions”, Arch. Elektrot., z.2, 1976, (in Polish), pp. 287-295.

  68. J. A. Starzyk, „Topological Methods of Analysis of LSL Networks with Nullators and Norators”, Prace Naukowe PW, Elektronika, No. 20, Warszawa, 1975, (in Polish), pp. 73-89.

  69. J. A. Starzyk, „Topological, Analysis of LSL Networks with Nullators and Norators; Impedance Dependencies”, Prace Naukowe PW, Elektronika, No. 20, Warszawa, 1975, (in Polish), pp. 61-71.

  70. J. A. Starzyk, „Complement of Columns of Constant-row Structural Number to the Factorizable Number”, Arch. Elektrot., z.2, 1975 (in Polish), pp. 237-244.

  71. Konczykowska and J. A. Starzyk „Determination of Structural Number of a Partitioned Graph. Part I and II.”, Arch. Elektrot z.2, 1975, (in Polish), pp. 245-262.

Prowadzone przedmioty:

Systemy Autonomiczne, Inteligencja Obliczeniowa, Projekt Zespołowy, Seminarium Dyplomowe

Prowadził również przedmioty: Digital Design, Analog and Digital VLSI, Computer Aided Analysis, Digital Test and Testable Design, VHDL Hardware Description Language with FPGA Design, Machine Intelligence.

Materiały dla studentów dostępne są na platformie Moodle.

 

zdjęcie prof. Starzyk

DANE KONTAKTOWE

Prof. dr hab. Janusz Starzyk
Katedra Zastosowań Systemów Informatycznych
36-020 Tyczyn, Kielnarowa
pok. KM202
Kontakt przez sekretarza Katedry dr inż. Leszka Gajeckiego
e-mail: lgajecki@wsiz.edu.pl
http://oucsace.cs.ohio.edu/~starzyk

KONSULTACJE

Link do konsultacji/link to consult : https://wsiz.webex.com/meet/jstarzyk

wtorek 19:05-20:35 online

 

Head of the Department of Information Systems Applications

Professor at the Department of Information Systems Applications

Employed at the University of Information Technology and Management in Rzeszów since 2007

Janusz A. Starzyk received M.S. degree in applied mathematics and Ph.D. degree in electrical engineering both from Warsaw University of Technology, Warsaw, Poland, and habilitation degree in electrical engineering from Silesian University of Technology in Gliwice, Poland. He worked as an Assistant Professor at the Institute of Electronics Fundamentals, Warsaw University of Technology, Warsaw, Poland. Subsequently, he spent two years as a Post-Doctorate Fellow and research engineer at McMaster University, Hamilton, Canada. Since 1991 he has been a professor of Electrical Engineering and Computer Science, at Ohio University, Athens, Ohio, USA, and a director of Embodied Intelligence Lab. Since 2009 he has been the head of Department of Applied Information Systems at WSIiZ, Rzeszow, Poland.

He cooperated with the National Institute of Standards and Technology, USA in the area of testing and fault diagnostics of analog and mixed circuits. He was a visiting professor at the University of Florence, Italy and at Nanyang Technological University, Singapore. He has been a consultant of Magnetek Corp. and a technical advisor and a senior researcher at Magnolia Broadband Inc. Italy. For several summers he was a visiting faculty at Wright Labs – Advanced Systems Research Group and Redstone Arsenal – US Army Test, Measurement and Diagnostic Activity. For a year, he was an IPA fellow at Wright Research Labs, Automatic Target Recognition Group. He was a visiting researcher at ATT Bell Laboratories – VLSI Systems Research Group and Sarnoff Research Labs. – Mixed Signal VLSI Design Group.

Research

Current research includes embodied machine intelligence, self-organizing goal driven learning, motivating mechanisms for sensory-motor interactions, associative spatio-temporal memories, episodic memories as well as applications of machine learning to intelligent control of robots and virtual agents, building virtual environments, pattern analysis, data mining, and financial markets.

Motivated Learning in Virtual Environments for Robots and Avatars

This research is focused on the design and development of computational systems that learn, operate and communicate in a changing, unpredictable environment by developing high-level sensory representation of the environment and developing their own motor abilities. The objective is to design biologically inspired, self-organizing, motivated learning (ML) and problem-solving tools in order to build powerful systems capable of formulating and achieving complex goals using limited computational resources.

Motivated learning will develop cognitive agents, which will analyze and act upon available information, seek further information and pursue predefined goals according to their own, internally developed behavioral patterns. The objective is to enhance understanding of the role of interaction with environment for perception, cognition and interaction. This development is based on the mechanism of motivated learning that uses self-organization, goal creation and goal driven learning (including hierarchical reinforcement and curiosity based learning). Research is focused on theoretical aspects of motivated cognitive systems development and their practical applications to robots and virtual agents.

An important question is how to motivate an agent to do anything, and in particular, to enhance its own complexity? An answer to this question may be provided by the motivated learning idea. ML yields EI agents that set their own goals and develop internal reward systems. By providing an agent with the internal drive to learn, set its own objectives, and evaluate success of its actions, motivated learning may lead to an intelligent behavior. A ML agent receives reinforcement from environment for its most primitive objectives; however, it does not suffer from quick growth of the learning effort in a complex environment. It uses a goal creation system (GCS) to define and manage abstract goals. ML improves agent’s ability to perceive useful objects and learn useful motor skills.

The webpage of current project „Developing of effective mechanisms for robot perception using motivated learning and self-organizing associative memory” : perception.wsiz.edu.pl

The webpage of project „Organization of Semantic and Episodic Memory in Motivated Learning of Robots”: ncn.wsiz.edu.pl

 

http://orcid.org/0000-0003-2678-5515

Some new publications are at the webpage of project „Developing of effective mechanisms for robot perception using motivated learning and self-organizing associative memory” : perception.wsiz.edu.pl

 

Chapters in books

  1. W. Galus, J. A. Starzyk, Świadomość? Ależ to bardzo proste! BEL Studio, Warszawa, 1, 2018. 372 pp.

  2. M. Jaszuk, J. A. Starzyk, “Building Internal Scene Representation in Cognitive Agents”, in Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions, series: Advances in Intelligent Systems and Computing, 364, Springer, pp 479-491, 2016.

  3. J. A. Starzyk and Basawaraj, “Comparison of two memristor based neural network learning schemes for crossbar architecture”, Advances in Computational Intelligence, Lecture Notes in Computer Science, vol. 7902, pp. 492-499, 2013.

  4. J. A. Starzyk, J. Graham, and L. Puzio, “Simulation of a Motivated Learning Agent”, Artificial Intelligence Applications and Innovations , Vol. 412, IFIP Advances in Information and Communication Technology, pp. 205-214, 2013.

  5. J. A. Starzyk, Motivated Learning for Computational Intelligence, in Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, edited by B. Igelnik, IGI Publishing, ch.11, pp. 265-292, 2011.

  6. J. A. Starzyk, Y. Liu, S. Batóg, “A Novel Optimization Algorithm Based on Reinforcement Learning”, in `Computational Intelligence in Optimization-Applications and Implementations’, Tenne, Yoel; Goh, Chi-Keong (Eds.), Springer Verlag, 2010, pp. 27-49.

  7. „Introduction to Computer Design and Analysis of Electronic Networks”, (Co‑author), Wydawnictwa PW, Warszawa, 1978 (in Polish).

  8. Co‑author of Polish translation „Computer‑aided analysis of electronic circuits, algorithms and computational techniques”, by L.O. Chua and P.M. Lin, Wydawnictwa Naukowo‑Techniczne, Warszawa, 1981

  9. „Advances in Circuits and Systems – Selected Papers on Analog Fault Diagnosis”, (co-author), IEEE Press, New York 1987.

  10. „Analog Methods for Circuit Analysis and Diagnosis”, (Co‑author), Marcel Dekker, Inc., New York, 1988.

  11. J. A. Starzyk, “Topological Analysis and Diagnosis of Analog Circuits”, Wydawnictwa Politechniki Slaskiej, 2008, 140 pp.

  12. J. A. Starzyk, „Motivation in Embodied Intelligence” in Frontiers in Robotics, Automation and Control, I-Tech Education and Publishing, Austria, 2008, pp. 83-110

 

 

Journal Papers

  1. Basawaraj, J. A. Starzyk, and A. Horzyk “Episodic Memory in a Minicolumn Associative Knowledge Graph”, submitted to IEEE Trans on Neural networks and Learning Systems.

  2. J. A. Starzyk, Ł. Maciura, and A. Horzyk, „Associative Memories with Synaptic Delays” accepted to IEEE Trans on Neural networks and Learning Systems.

  3. A. Horzyk, J. A. Starzyk, J. Graham, “Integration of Semantic and Episodic Memories”, IEEE Trans on Neural networks and Learning Systems, vol. 28, no. 12, Dec. 2017, pp. 3084-3095, DOI: 10.1109/TNNLS.2017.2728203.

  4. J. A. Starzyk, J. Graham, L. Puzio, “Needs, Pains, and Motivations in a Simulated Learning Agent,” IEEE Trans on Neural networks and Learning Systems, vol. 28, no.11, Nov. 2017, pp. 2528-2540.

  5. J. A. Starzyk, J. Graham, “MLECOG – Motivated Learning Embodied Cognitive Architecture” IEEE Systems Journal, vol. 11, no. 3, 2017, pp. 1272-1283.

  6. T.-H. Teng, A.-H. Tan, J. A. Starzyk, “Multi-Agent Reinforcement Learning with Task Dependencies and Resource Competition”, submitted to IEEE Trans on Neural Networks and Learning Systems, 2015.

  7. J. Graham, J. A. Starzyk, and D. Jachyra, “Opportunistic Behavior in Motivated Learning Agents”, IEEE Trans on Neural networks and Learning Systems, vol. 26, no. 8, Aug. 2015, pp. 1735-1746.

  8. J. A. Starzyk, and Basawaraj, “Memristor Crossbar Architecture for Synchronous Neural Networks”, IEEE Trans. Circuits and Systems, Part I, March 2014, pp.1-12.

  9. V. A. Nguyen, J. A. Starzyk and W-B. Goh,”A Spatio-temporal Long-term Memory Approach for Visual Place Recognition in Mobile Robotic Navigation” –Robotics and Autonomous Systems, Elsevier, vol. 61, no. 12, Dec. 2013, pp. 1744–1758.

  10. W. Wang, B. Subagdja, A.-H. Tan, and J. A. Starzyk, “Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting” IEEE Trans on Neural Networks and Learning Systems, vol. 23, no. 10, Oct. 2012, pp. 1574 – 1586.

  11. T. Huang, C. Li, S. Duan, and J. A. Starzyk, „Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects”  IEEE Trans on Neural Networks and Learning Systems, vol. 23 , no. 6 , June 2012, pp.  866 – 875.<

  12. V. A. Nguyen, J. A. Starzyk, W-B. Goh, D. Jachyra, “Neural Network Structure for Spatio-Temporal Long-Term Memory” IEEE Trans on Neural Networks and Learning Systems, vol. 23 no. 6, June, 2012, pp. 971-983.

  13. J. A. Starzyk, J. T. Graham, P. Raif, and A-H.Tan, “Motivated Learning for Autonomous Robots Development”, Cognitive Science Research, v.14, no.1, 2012, p.10(16) pp. 10-25.

  14. P. Moghadam, J. A. Starzyk, and W. S. Wijesoma, „Fast Vanishing Point Detection in Unstructured Environments” IEEE Trans. on Image Processing, vol. 21, no. 1, Jan 2012, pp. 425-430.

  15. J. A. Starzyk and D. Prasad, “A Computational Model of Machine Consciousness” International Journal of Machine Consciousness, vol. 3, No. 2 (2011) pp. 255-281.

  16. J. Feng, W-H. Qiu, J. Starzyk, “Risk Assessment of Credit Card Application Based on Self-organizing Learning Array (SOLAR)”, Industrial Engineering Journal, vol. 13, no. 6, pp. 71-75. Nov. 2010

  17. J. A. Starzyk, H. He, “Spatio-Temporal Memories for Machine Learning: A Long-Term Memory Organization”, IEEE Trans. on Neural Networks, vol. 20, no. 5, May 2009, pp 768 – 780.

  18. H. He, X. Shen, J. A. Starzyk, ”Power Quality Disturbances Analysis Based on EDMRA Method”, accepted for Int. Journal of Electrical Power & Energy Systems, vol. 31, no.5, May 2009.

  19. Y. Liu, J. A. Starzyk, Z. Zhu, “Optimized Approximation Algorithm in Neural Network without Overfitting”, IEEE Trans. on Neural Networks, vol. 19, no. 4, June, 2008, pp. 983-995.

  20. H. F. A. Hamed, S. Kaya, J. A. Starzyk, “Use of nano-scale double-gate MOSFETs in low-power tunable current mode analog circuits” Analog Integrated Circuits and Signal Processing, Feb., 2008.

  21. J. A. Starzyk, and H. He, “Anticipation-Based Temporal Sequences Learning in Hierarchical Structure”, IEEE Trans. on Neural Networks, vol. 18,  no. 2,  March 2007, pp. 344 – 358. Received the best research paper award in the College of Engineering and Technology at Ohio University.

  22. J. A. Starzyk and H. He, “A Novel Low Power Logic Circuit Design Scheme,” IEEE Trans. Circuits Syst. II, vol. 54, no. 2, pp.176-180, Feb. 2007.

  23. Z. Zhu, F. van Graas and J. A. Starzyk, “GPS signal acquisition using the repeatability of successive code phase measurements” GPS Solutions, Springer, May 2007.

  24. S. Kaya, H. F. A. Hamed and J. A. Starzyk, “Low-Power Tunable Analog Circuit Blocks Based on Nanoscale Double-Gate MOSFETs,” IEEE Trans. Circuits Syst. II, vol. 54, no. 7, July 2007, pp. 571-575.

  25. J. A. Starzyk, H. He, and Y. Li, “A Hierarchical Self-organizing Associative Memory for Machine Learning”, Lecture Notes in Computer Science 4491: pp. 413-423, 2007.

  26. J. A. Starzyk, Y. Liu, D. Vogel, ”Sparse Coding in Sparse Winner Networks”, Lecture Notes in Computer Science 4492: pp. 534-541, 2007.

  27. Z. Zhu, H. He, J.A. Starzyk, and C. Tseng “Self-Organizing Learning Array and its Application to Economic and Financial Problems” Elsevier Science, Information Sciences, vol. 177, no 5, 1 March 2007, Pages 1180-1192.

  28. J. A. Starzyk, M. Ding, Y. Liu, ”Hybrid Pipeline Structure for Self-Organizing Learning Array”, Lecture Notes in Computer Science, 2007.

  29. H. He, and J. A. Starzyk, “Online Dynamic Value System for Machine Learning”, Lecture Notes in Computer Science 4491: pp. 441-448, 2007.

  30. J. A. Starzyk, Z. Zhu, and Y. Li, „Associative Learning in Hierarchical Self Organizing Learning Arrays“, IEEE Trans. Neural Networks, vol.17, no. 6, pp.1460-1470, Nov. 2006.

  31. H. He and J. A. Starzyk, „A Self Organizing Learning Array System for Power Quality Classification based on Wavelet Transform„, IEEE Trans. on Power Delivery, Jan. 2006.

  32. J. A. Starzyk, Z. Zhu and T.-H. Liu „Self-Organizing Learning Array” IEEE Trans. on Neural Networks, vol. 16, no. 2, pp. 355-363, March 2005.  

  33. J. A. Starzyk, Z. Zhu, and Y. Li, „Associative Learning in Hierarchical Self Organizing Learning Arrays„, Artificial Neural Networks: Biological Inspirations. Lecture Notes in Computer Science 3696: pp. 91-96, 2005.

  34. Janusz A. Starzyk, and Yue Li, David D. Vogel, „Neural Network with Memory and Cognitive Functions„, Artificial Neural Networks: Biological Inspirations. Lecture Notes in Computer Science 3696: pp. 85-90, 2005.

  35. J. A. Starzyk, Dong Liu, Zhi-Hong Liu, D. Nelson, and J. Rutkowski, “Entropy-based optimum test points selection for analog fault dictionary techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 3, June 2004, pp. 754-761.

  36. J. A. Starzyk and F. Wang, „Dynamic Probability Estimator for Machine Learning” IEEE Trans. on Neural Networks, vol.15, no 2, March 2004, pp.298-308.  

  37. J. A. Starzyk, R. P. Mohn, and L. Jing, L., „A Cost-Effective Approach to the Design and Layout of a 14-b Current-Steering DAC Macrocell„, IEEE Trans. on Circuits and Systems I: Fundamental Theory and Applications, Vol. 51 ,  no. 1, Jan. 2004, pp. 196 – 200.

  38. D. E. Nelson, J. A. Starzyk, and D. D. Ensley, „Iterated wavelet transformation and signal discrimination for HRR radar target recognition„,  IEEE Trans. on Systems, Man and Cybernetics, Part A  ,Vol. 33 , no.1 , Jan. 2003 , pp. 52 – 57.

  39. D. Liu and J. A. Starzyk, ” A generalized fault diagnosis in dynamic analog circuits” Int. Journal of Circuit Theory and Applications, vol. 30, 2002, pp. 487-510.

  40. D. E. Nelson, J. A. Starzyk, and D. D. Ensley, „Iterative Wavelet Transformation and Signal Discrimination for HRR Radar Target Recognition,” Multidimensional Systems and Signal Processing, Vol. 14, no.2. 2002.

  41. J. Becker, A. Alsolaim, M. Glesner, and J. Starzyk, “A Parallel Dynamically Reconfigurable Architecture for Flexible Aplication-Tailored Hardware/Software Systems in Future Mobile Communication”, The Journal of Supercomputing, Erratum Vol. 23, 132, 2002,  19(1): 105-127 (2001).

  42. J. Pang and J. A. Starzyk, „Fault Diagnosis in Mixed-Signal Low Testability System” An Int. Journal of Analog Integrated Circuits and Signal Processing, vol. 28, no.2, August 2001, pp. 159-170.

  43. J. A. Starzyk and Y.-W. Jan, and F. Qiu, „A DC-DC Charge Pump Based on Voltage Doublers„, IEEE Trans. Circuits and Systems, Part I, vol. 48, no. 3, March 2001, pp. 350-359.

  44. G. N. Stenbakken, D. Liu J. A. Starzyk, and B. C. Waltrip, „Nonrandom Quatization Errors in Timebases„, IEEE Trans. on Instrumentation and Measurement, vol. 50, no. 4, Aug. 2001, pp.888-892.

  45. J. A. Starzyk, D. E. Nelson, and K. Sturtz, ” A Mathematical Foundation for Improved Reduct Generation in Information Systems„, Journal of Knowledge and Information Systems, v. 2 n. 2, March 2000 p.131-146.

  46. J. A. Starzyk, J. Pang, S. Manetti, G. Fedi, and C. Piccirilli, „Finding Ambiguity Groups in Low Testability Analog Circuits„, IEEE Trans. Circuits and Systems, Part I, vol 47, no. 8, 2000, pp. 1125-1137.

  47. G. Fedi, S. Manetti, J. A. Starzyk, M. C. Piccirilli „Determination of an Optimum Set of Testable Components in the Fault Diagnosis of Analog Circuits„, IEEE Trans. Circuits and Systems, Part I, vol. 46, no.7, 1999, 779-787.

  48. J. A. Starzyk, D. E. Nelson, and K. Sturtz, „Reduct Generation in Information Systems„, Bulletin of International Rough Set Society, 1999, 3 (1/2).

  49. J. A. Starzyk, „Hierarchical Analysis of High Frequency Interconnect Networks„, IEEE Trans. on Computer Aided Design of Integrated Circuits and Systems, vol.13, no.5, 1994, pp. 658-664.

  50. J. A. Starzyk and X. Fang, „A CMOS Current Mode Winner-Take-All Circuit with both Excitatory and Inhibitory Feedback„, Electronics Letters, 1993.

  51. G. N. Stenbakken and J. A. Starzyk, „Diakoptic and Large Change Sensitivity Analysis„, IEE Proc. G, Circuits, Devices and Systems, vol. 139, no.1, 1992, pp.114-118.

  52. J. A. Starzyk and H. Dai, „A Decomposition Approach for Testing Large Analog Networks,” Journal of Electronic Testing – Theory and Applications, no.3, 1992, pp. 181-195.

  53. J. A. Starzyk and A. Konczykowska, „Flowgraph Analysis of Large Electronic Networks„, IEEE Trans. on Circuits and Systems, vol. CAS-33, 1986.

  54. J. A. Starzyk and E. Sliwa, „Upward Topological Analysis of Large Circuits Using Directed Graph Representation„, IEEE Trans. on Circuits and Systems, vol. CAS-31, 1984, pp. 410-414.

  55. E. Salama, J. A. Starzyk and J. W. Bandler, „A Unified Decomposition Approach for Fault Location in Large Analog Circuits„, IEEE Trans. on Circuits and Systems, vol. CAS-31, 1984, pp. 609-622.

  56. J. A. Starzyk, R. M. Biernacki and J. W. Bandler, „Evaluation of Faulty Elements within Linear Subnetworks„, Int. Journal of Circuit Theory and Applications, vol. 12, 1984, pp. 23-37.

  57. J. A. Starzyk and J. W. Bandler, „Multiport Approach to Multiple-Fault Location in Analog Circuits„, IEEE Trans. on Circuits and Systems, vol. CAS-30, 1983, pp. 762-765.

  58. J. A. Starzyk, „An Efficient Cluster Algorithm”, Acta Polytechnica, CVUT, Praha, 1981, pp. 49-55.

  59. J. A. Starzyk, „Signal Flow-Graph Analysis by Decomposition Method„, IEE Proc. on Electronic Circuits and Systems, No. 2, April 1980, pp. 81-86.

  60. G. Centkowski and J. A. Starzyk, „Topological Synthesis of LLF Networks”, Acta Polytechnica, CVUT, Praha, 1980, pp. 77-86.

  61. J. A. Starzyk and E. Sliwa, „Hierarchic Decomposition Method for the Topological Analysis of Electronic Networks„, Int. Journal of Circuit Theory and Applications, Vol. 8, 1980, pp. 407-417.

  62. J. A. Starzyk, „Generation of Complete Trees by the Method of Modified Structural Matrix”, Arch. Elektrot., z.4, 1978, (in Polish), pp. 843-852.

  63. J. A. Starzyk, „New Method for Designing Complete Trees of a Pair of Conjugate Graphs”, Arch. Elektrot., z.1, 1977, (in Polish), pp. 41-46.

  64. J. A. Starzyk, „Application of the Controlled Expansions Method to the Topological Analysis of Circuits”, Arch. Elektrot., z.1, 1977, (in Polish), pp. 47-58.

  65. J. A. Starzyk, „Determination of the Nullator-Norator Graph’s Complete Trees”, Radio Electronics and Communication Systems, t.XX 12, 1977, (in Russian), pp. 9-15.

  66. J. A. Starzyk, and J. Wojciechowski, „Topological Analysis and Synthesis of Electrical Networks by the Method of Structural Numbers”, Raport Naukowy IPE, Warszawa, 1977, (in Polish).

  67. J. A. Starzyk, „Topological Synthesis of Linear Active Networks Described by Multivariable Functions”, Arch. Elektrot., z.2, 1976, (in Polish), pp. 287-295.

  68. J. A. Starzyk, „Topological Methods of Analysis of LSL Networks with Nullators and Norators”, Prace Naukowe PW, Elektronika, No. 20, Warszawa, 1975, (in Polish), pp. 73-89.

  69. J. A. Starzyk, „Topological, Analysis of LSL Networks with Nullators and Norators; Impedance Dependencies”, Prace Naukowe PW, Elektronika, No. 20, Warszawa, 1975, (in Polish), pp. 61-71.

  70. J. A. Starzyk, „Complement of Columns of Constant-row Structural Number to the Factorizable Number”, Arch. Elektrot., z.2, 1975 (in Polish), pp. 237-244.

  71. Konczykowska and J. A. Starzyk „Determination of Structural Number of a Partitioned Graph. Part I and II.”, Arch. Elektrot z.2, 1975, (in Polish), pp. 245-262.

Conducted courses:

Inteligent Autonomous Systems (lecture), Team Project, Diploma Seminary

In addition courses in Digital Design, Analog and Digital VLSI, Computer Aided Analysis, Digital Test and Testable Design, VHDL Hardware Description Language with FPGA Design, Machine Intelligence.

Materials for students are available at the relevant courses at the Moodle platform.

 

zdjęcie prof. Starzyk

Contact data

Prof. Dr. Hab. Janusz A. Starzyk
Department of Information Systems Applications
36-020 Tyczyn, Kielnarowa
room KM202
Contact by the secretary of the Department Dr. Inż. Leszek Gajecki
e-mail: lgajecki@wsiz.edu.pl
http://oucsace.cs.ohio.edu/~starzyk

Office hours

Link to consults : https://wsiz.webex.com/meet/jstarzyk

Tuesday 19:05-20:35 online

 

Czy wiesz, że Wyższa Szkoła Informatyki i Zarządzania w Rzeszowie należy do czołówki najlepszych uczelni w Polsce? Oferujemy kształcenie praktyczne, dostosowane do trendów panujących na rynku pracy. Badania dowodzą, że nasi absolwenci szybko znajdują dobrze płatną pracę i są zadowoleni ze studiów.

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35-225 Rzeszów

Telefon: 17 866 11 11

E-mail: wsiz@wsiz.edu.pl

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