Skip to main content
SpringerLink
Log in

Introduction to Perception Based Computing

  • Chapter
Emerging Paradigms in Machine Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 13))

Abstract

We present a general scheme of interaction and we discuss the role of interactions in modeling of perception processes. We also discuss the role of information systems in interactive computing used to build perception modeling. In particular, we illustrate use of information systems for representation of actions or plans, their (changing in time) pre and post conditions. These information systems create a starting point for perception modeling, i.e., modeling of the process of understanding of sensory measurements.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbasian, H., Drummond, C., Japkowicz, N., Matwin, S.: Robustness of Classifiers to Changing Environments. In: Farzindar, A., Kešelj, V. (eds.) Canadian AI 2010. LNCS, vol. 6085, pp. 232–243. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  2. Arbib, M.A.: The Metaphorical Brain 2: Neural Networks and Beyond. Willey & Sons (1989)

    Google Scholar 

  3. Bara, B.G.: Cognitive Science. A Developmental Approach to the Simulation of the Mind. Lawrence Erlbaum Associates, Hove (1995)

    Google Scholar 

  4. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers (2002)

    Google Scholar 

  5. Barr, B.: *-Autonomous categories. Lecture Notes in Mathematics, vol. 752. Springer (1979)

    Google Scholar 

  6. Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press (1997)

    Google Scholar 

  7. Bazan, J.G.: Hierarchical Classifiers for Complex Spatio-Temporal Concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Bazan, J.G., Skowron, A.: On-Line Elimination of Non-Relevant Parts of Complex Objects in Behavioral Pattern Identification. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 720–725. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Blass, A., Gurevich, Y.: Abstract State Machines Capture Parallel Algorithms. ACM Transactions on Computational Logic 4(4), 578–651 (2003)

    Article  MathSciNet  Google Scholar 

  10. Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press (2001)

    Google Scholar 

  11. http://chu.stanford.edu/

  12. De Raedt, L.: Logical and Relational Learning (2008)

    Google Scholar 

  13. Gibson, J.J.: The perception of the visual world. Houghton Mifflin (1950)

    Google Scholar 

  14. Gibson, J.J.: The ecological approach to visual perception. Lawrence Earlbaum Assoc. Publishers (1979)

    Google Scholar 

  15. Goldstein, E.B. (ed.): Blackwell Handbook of Perception. Blackwell Publishers Ltd. (2001)

    Google Scholar 

  16. Goldin, D.Q., Smolka, S.A., Attie, P.C., Sonderegger, E.L.: Turing Machines, Transition Systems, and Interaction. Information and Computation 194(2), 101–128 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Goldin, D., Smolka, S., Wegner, P.: Interactive Computation: The New Paradigm. Springer, Heidelberg (2010)

    Google Scholar 

  18. Goldin, D.Q., Wegner, P.: Principles of interactive computation. In: Goldin, D., Smolka, S., Wegner, P. (eds.) Interactive Computation: The New Paradigm, pp. 25–37. Springer (2006)

    Google Scholar 

  19. Gurevich, Y.: Evolving Algebra 1993: Lipari Guide. In: Bögger, E. (ed.) Specification and Validation Methods, pp. 9–36. Oxford University Press (1995)

    Google Scholar 

  20. Gurevich, Y.: Sequential Abstract State Machines Capture Sequential Algorithms. ACM Trans. on Computational Logic 1(1), 77–111 (2000)

    Article  MathSciNet  Google Scholar 

  21. Gurevich, Y.: Interactive Algorithms 2005. In: Goldin, D., Smolka, S., Wegner, P. (eds.) Interactive Computation: The New Paradigm, pp. 165–181. Springer (2006)

    Google Scholar 

  22. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer (2008)

    Google Scholar 

  23. Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. In: Peters et.al, [54], pp. 94–132

    Google Scholar 

  24. Jankowski, A., Skowron, A.: Logic for artificial intelligence: The Rasiowa - Pawlak school perspective. In: Ehrenfeucht, A., Marek, V., Srebrny, M. (eds.) Andrzej Mostowski and Foundational Studies, pp. 106–143. IOS Press, Amsterdam (2008)

    Google Scholar 

  25. Jankowski, A., Skowron, A.: Wisdom Technology: A Rough-Granular Approach. In: Marciniak, M., Mykowiecka, A. (eds.) Bolc Festschrift. LNCS, vol. 5070, pp. 3–41. Springer, Heidelberg (2009)

    Google Scholar 

  26. Keefe, R.: Theories of Vagueness. Cambridge Studies in Philosophy (2000)

    Google Scholar 

  27. Kleijn, J., Koutny, M.: A Petri net model for membrane systems with dynamic structure. Natural Computing 8, 781–796 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  28. Liu, J.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning, self-Organization and Adaptive Computation. World Scientific Publishing (2001)

    Google Scholar 

  29. Liu, H., Motoda, H.: Feature Extraction, Construction and Selection: A Data Mining Perspective. Springer (1998)

    Google Scholar 

  30. Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Freeman (1982)

    Google Scholar 

  31. Michalski, R.S.: Pattern recognition as knowledge-guided computer induction. Technical Report No. 927. Department of Computer Science. University of Illinois, Urbana-Champaign (1978)

    Google Scholar 

  32. Milner, R.: Elements of interaction. Communications of ACM 36, 78–89 (1993)

    Article  Google Scholar 

  33. Newell, A.: Unified theories of cognition. Harvard University Press (1990)

    Google Scholar 

  34. Nguyen, S.H., Bazan, J.G., Skowron, A., Nguyen, H.S.: Layered Learning for Concept Synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  35. Nguyen, T.T., Skowron, A.: Rough-granular computing in human-centric infor-mation processing. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling. SCI, vol. 182, pp. 1–30. Springer (2009)

    Google Scholar 

  36. Noë, A.: Action in Perception. MIT Press (2004)

    Google Scholar 

  37. Omicini, A., Ricci, A., Viroli, M.: The Multidisciplinary Patterns of Interaction from Sciences to Computer Science. In: Goldin, D., Smolka, S., Wegner, P. (eds.) Interactive Computation: The New Paradigme, pp. 359–414. Springer, Heidelberg (2010)

    Google Scholar 

  38. Păun, G.: Membrane Computing. An Introduction. Springer (2002)

    Google Scholar 

  39. Pawlak, Z.: Information Systems – theoretical foundation. Information Systems 6, 205–218 (1981)

    Article  MATH  Google Scholar 

  40. Pawlak, Z.: Rough sets. International Journal of Computing and Information Sciences 18, 341–356 (1982)

    Article  MathSciNet  Google Scholar 

  41. Pawlak, Z.: Rough sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers (1991)

    Google Scholar 

  42. Pawlak, Z.: Concurrent versus sequential the rough sets perspective. Bulletin of the EATCS 48, 178–190 (1992)

    MATH  Google Scholar 

  43. Pawlak, Z.: Elementary rough set granules: toward a rough set processor. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, pp. 5–13. Springer (2003)

    Google Scholar 

  44. Pawlak, Z.: Some Issues on Rough Sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1–58. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  45. Pawlak, Z., Skowron, A.: A rough set approach for decision rules generation. In: Thirteenth International Joint Conference on Artificial Intelligence IJCAI 1993, pp. 114–119. Morgan Kaufmann (1993)

    Google Scholar 

  46. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Science 177, 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  47. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Science 177, 28–40 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  48. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Science 177, 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  49. Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons (2008)

    Google Scholar 

  50. Peters, J.F., Henry, C.D., Ramanna, S.: Reinforcement learning in swarms that learn. In: Barthes, J.-P., Jain, L., Skowron, A., Sun, R., Morizet-Mahoudeaux, P., Liu, J., Zhong, N. (eds.) Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Compiegne, France, September 19-22, pp. 400–406. IEEE Computer Society Press, Los Alamitos (2005)

    Chapter  Google Scholar 

  51. Peters, J.F., Henry, C.D., Ramanna, S.: Reinforcement learning with pattern-based rewards. In: Hamza, M.H. (ed.) Computational Intelligence, Proceedings of the Fourth International IASTED Conference on Computational Intelligence, July 4-6, pp. 267–272. IASTED/ACTA Press, Calgary (2005)

    Google Scholar 

  52. Peters, J.F., Lockery, D., Ramanna, S.: Monte-Carlo off-policy reinforcement learning: A rough set approach. In: Nedjah, N., de Mourelle, M.L., Abraham, A., Köppen, M. (eds.) 5th International Conference on Hybrid Intelligent Systems (HIS 2005), Rio de Janeiro, Brazil, November 6-9, pp. 187–192 (2005)

    Google Scholar 

  53. Peters, J.F., Henry, C., Ramanna, S.: Rough ethograms: A study of intelligent system behavior. In: Kłopotek, M.A., Wierzchoń, S., Trojanowski, K. (eds.) Intelligent Information Systems. AISC, vol. 31, pp. 117–126. Springer, Heidelberg (2005)

    Google Scholar 

  54. Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.): Transactions on Rough Sets VI. LNCS, vol. 4374. Springer, Heidelberg (2007)

    Google Scholar 

  55. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)

    MathSciNet  MATH  Google Scholar 

  56. Polkowski, L., Skowron, A., Żytkow, J.: Rough foundations for rough sets. In: Proceedings of The Third International Workshop on Rough Sets and Soft Computing (RSSC 1994), November 10-12, pp. 142–149. San Jose State University, CA (1994); see also: Rough foundations for rough sets. In: Lin, T.Y., Wildberger, A.M. (eds.): Soft Computing, Simulation Councils, Inc., San Diego, pp. 55–58 (1995)

    Google Scholar 

  57. Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15, 333–365 (1997)

    Article  MathSciNet  Google Scholar 

  58. Read, S.: Thinking about logic: An introduction to the philosophy of logic. Oxford University Press (1995)

    Google Scholar 

  59. Ray, D.: A Game-Theoretic Perspective on Coalition Formation. Oxford University Press (2007)

    Google Scholar 

  60. Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artificial Intelligence 101, 165–200 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  61. Simons, H.: The Science of the Artificial, 2nd edn. MIT Press (1982)

    Google Scholar 

  62. Skowron, A.: Rough sets and vague concepts. Fundamenta Informaticae 64(1-4), 417–431 (2005)

    MathSciNet  MATH  Google Scholar 

  63. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)

    MathSciNet  MATH  Google Scholar 

  64. Skowron, A., Stepaniuk, J.: Informational granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, pp. 43–84. Springer (2003)

    Google Scholar 

  65. Skowron, A., Stepaniuk, J.: Hierarchical modelling in searching for complex patterns: constrained sums of information systems. Journal of Experimentaland Theoretical Artificial Intelligence 17(1-2), 83–102 (2005)

    Article  MATH  Google Scholar 

  66. Skowron, A., Stepaniuk, J.: Approximation Spaces in Rough Granular Computing. Fundamenta Informaticae 100, 141–157 (2010)

    MathSciNet  MATH  Google Scholar 

  67. Skowron, A., Stepaniuk, J.: Rough Granular Computing Based on Approximation Spaces (submitted)

    Google Scholar 

  68. Skowron, A., Suraj, Z.: Rough sets and concurrency. Bulletin of the Polish Academy of Sciences 41, 237–254 (1993)

    MATH  Google Scholar 

  69. Skowron, A., Suraj, Z.: Discovery of concurrent data models from experimental tables: A rough set approach. In: Proceedings of First International Conference on Knowledge Discovery and Data Mining, pp. 288–293. AAAI Press (1995)

    Google Scholar 

  70. Skowron, A., Szczuka, M.: Toward interactive computations: A rough-granular approach. In: Koronacki, J., Wierzchon, S., Ras, Z., Kacprzyk, J. (eds.) Commemorative Volume to Honor Ryszard Michalski, pp. 1–20. Springer (2009)

    Google Scholar 

  71. Skowron, A., Wang, H., Wojna, A., Bazan, J.G.: A Hierarchical Approach to Multimodal Classification. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 119–127. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  72. Skowron, A., Wasilewski, P.: Information Systems in Modeling Interactive Computations on Granules. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 730–739. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  73. Skowron, A., Wasilewski, P.: An Introduction to Perception Based Computing. In: Kim, T.-H., Lee, Y.-H., Kang, B.-H., Ślęzak, D. (eds.) FGIT 2010. LNCS, vol. 6485, pp. 12–25. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  74. Skowron, A., Wasilewski, P.: Information systems in interactive computing. In: Wakulicz-Deja, A. (ed.) Proceedings of Desision System Support Conference, Instituite of Informatics, Zakopane, December 1–4, pp. 33–45. Silesian University (2010)

    Google Scholar 

  75. Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. Theoretical Computer Science 412(42), 5939–5959 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  76. Skowron, A., Wasilewski, P.: Toward interactive rough–granular computing. Control & Cybernetics 40(2), 1–23 (2011)

    Google Scholar 

  77. Skowron, A., Stepaniuk, J., Peters, J.F.: Towards discovery of relevant patterns from parametrized schemes of information granule construction. In: Inuiguchi, M., Hirano, S., Tsumoto, S. (eds.) Rough Set Theory and Granular Computing, pp. 97–108. Springer (2003)

    Google Scholar 

  78. Skowron, A., Stepaniuk, J., Peters, J., Swiniarski, R.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3), 363–378 (2006)

    MathSciNet  MATH  Google Scholar 

  79. Słowiński, R., Vanderpooten, D.: Similarity relation as a basis for rough approximations. In: Wang, P. (ed.) Advances in Machine Intelligence and Soft Computing, vol. 4, pp. 17–33. Duke University Press, Duke (1997)

    Google Scholar 

  80. Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer (2008)

    Google Scholar 

  81. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)

    Google Scholar 

  82. Sycara, K., Norman, T.J., Giampapa, J.A., Kollingbaum, M., Burnett, C., Masato, D., McCallum, M., Strub, M.H.: Agent Support for Policy-driven Collaborative Mission Planning. The Computer Journal 53, 528–540 (2010)

    Article  Google Scholar 

  83. Ślęzak, D., Toppin, G.: Injecting domain knowledge into a granular database engine – A position paper. In: CIKM 2010 (2010)

    Google Scholar 

  84. Ślęzak, D., Wasilewski, P.: Granular Sets Foundations and Case Study of Tolerance Spaces. In: ICSR 2006. LNCS (LNAI), vol. 4039, pp. 461–469. Springer, Heidelberg (2007)

    Google Scholar 

  85. Thagard, P.: Mind: Introduction to Cognitive Science, 2nd edn. MIT Press (2005)

    Google Scholar 

  86. Turing, A.: Computing machinery and intelligence. Mind 59, 33–60 (1950)

    MathSciNet  Google Scholar 

  87. Vapnik, V.: Statisctical Learning Theory. John Wiley & Sons (1998)

    Google Scholar 

  88. Vapnik, V.: Learning Has Just Started an interview with Prof. Vladimir Vapnik by R. Gilad- Bachrach (2008), http://learningtheory.org

  89. Wang, H., Lewis, M., Chien, S., Scerri, P., Velagapudi, P., Sycara, K., Kane, B.: Teams organization and performance in multi-human/multi-robot teams. In: 2010 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1617–1623 (2010)

    Google Scholar 

  90. Wang, Y., Kinsner, W., Zhang, D.: Contemporary Cybernetics and its Faces of Cognitive Informatics and Computational Intelligence. IEEE Trans. on System, Man, and Cybernetics (B) 39(4), 1–11 (2009)

    Google Scholar 

  91. Wasilewski, P., Ślęzak, D.: Foundations of Rough Sets from Vagueness Perspective. In: Hassanien, A.E., Suraj, Z., Ślęzak, D., Lingras, D.P. (eds.) Rough Computing. Theories, Technologies and Applications. Information Science Refer., pp. 1–37 (2008)

    Google Scholar 

  92. Wegner, P.: Why interactions is more powerful than algorithms. Communications of ACM 40, 81–91 (1997)

    Article  Google Scholar 

  93. Wegner, P.: Interactive foundation of computing. Theoretical Computer Science 192, 315–351 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  94. Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R., Yager, R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland Publishing Co. (1979)

    Google Scholar 

  95. Zadeh, L.A.: From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems 45, 105–119 (1999)

    MathSciNet  Google Scholar 

  96. Zadeh, L.A.: A new direction in AI-toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron & Piotr Wasilewski

Authors
  1. Andrzej Skowron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Wasilewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrzej Skowron .

Editor information

Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Skowron, A., Wasilewski, P. (2013). Introduction to Perception Based Computing. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28699-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28698-8

  • Online ISBN: 978-3-642-28699-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation