Skip to main content
SpringerLink
Log in

Neural Fuzzy Intelligent Agents

  • Chapter
Computing with Words in Information/Intelligent Systems 2

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 34))

  • 299 Accesses

Abstract

An intelligent agent must both learn what a user likes or dislikes and search databases on behalf of the user. A neural fuzzy system can learn an agent profile of a user when it samples user question-answer data. A fuzzy system uses if-then rules to store and compress the agent’s knowledge of the user’s likes and dislikes. A neural system uses training data to form and tune the rules. The profile is a preference map or a bumpy utility surface defined over the space of search objects. Rules define fuzzy patches that cover the surface bumps as learning unfolds and as the fuzzy agent system gives a finer approximation of the profile. The agent system searches for preferred objects with the learned profile and with a new fuzzy measure of similarity. The appendix derives the supervised learning law that tunes this matching measure with fresh sample data. We test the fuzzy agent profile system on object spaces of flowers and sunsets and test the fuzzy agent matching system on an object space of sunset images. Rule explosion and data acquisition impose fundamental limits on the system designs as they do for all fuzzy systems.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Y. S. Abu-Mostafa, “Learning from Hints in Neural Networks,” Journal of Complexity, pp. 192–198, 1990.

    Google Scholar 

  2. Y. S. Abu-Mostafa, “Hints,” Neural Computation, pp. 639–671, 1995.

    Google Scholar 

  3. R. Brooks, “Intelligence Without Reason,” in The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, L. Steels and R. Brooks, Eds., chapter 2, pp. 25–81. Lawrence Erlbaum Associates, Inc., 1995.

    Google Scholar 

  4. T. Caelli and D. Reye, “On the Classification of Image Regions by Colour, Texture and Shape,” Pattern Recognition, vol. 26, no. 4, pp. 461–470, 1993.

    Google Scholar 

  5. S. F. Chang and J. R. Smith, “Extracting Multi-Dimensional Signal Features for Content-Based Visual Query,” in SPIE Symposium on Visual Communications and Signal Processing, May 1995.

    Google Scholar 

  6. A. Chavez and P. Maes, “Kasbah: An Agent Marketplace for Buying and Selling Goods,” in Proceedings of the Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, April 1996.

    Google Scholar 

  7. M. Colombetti and M. Dorigo, “Training Agents to Perform Sequential Behavior,” Adaptive Behavior, vol. 2, no. 3, pp. 247–275, 1994.

    Google Scholar 

  8. W. D. Cook and M. Kress, Ordinal Information and Preference Structures: Decision Models and Applications, Prentice Hall, Englewood Cliffs, New Jersey, 1992.

    Google Scholar 

  9. G. Debreu, “Representation of a Preference Ordering by a Numerical Function,” in Mathematical Economics: Twenty Papers of Gerard Debreu, chapter 6, pp. 105–110. Cambridge University Press, 1983.

    Google Scholar 

  10. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 26, no. 1, pp. 29–41, February 1996.

    Article  Google Scholar 

  11. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1993.

    Google Scholar 

  12. B. V. Funt, “Color Constant Color Indexing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 5, pp. 522–529, May 1995.

    Article  Google Scholar 

  13. W. I. Grosky, “Multimedia Information Systems,” IEEE Multimedia, vol. 1, no. 1, pp. 12–24, Spring 1994.

    Google Scholar 

  14. P. T. Harker, “Incomplete Pairwise Comparison in the Analytic Hierarchy Process,” Mathematical Modelling, vol. 9, no. 11, pp. 837–848, 1987.

    MathSciNet  Google Scholar 

  15. W. Hildenbrand and A. P. Kirman, Introduction to Equilibrium Analysis, North Holland, 1976.

    Google Scholar 

  16. N. R. Jennings and M. Wooldridge, “Software Agents,” IEE Review, vol. 42, no. 1, pp. 17–20, January 1996.

    Article  Google Scholar 

  17. R. L. Keeney and H. Raiffa, Decision with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, New York, 1976.

    Google Scholar 

  18. M. Kendall and J. D. Gibbons, Rank Correlation Methods, Edward Arnold, A division of Hodder & Stoughton, London, fifth edition, 1990.

    Google Scholar 

  19. F. A. Kilpatrick, G. H. Gunsch, and E. Santos Jr., “Induction and State-Space Search for an Intelligent Training System,” in Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference, 1996.

    Google Scholar 

  20. H. M. Kim and B. Kosko, “Neural Fuzzy Motion Estimation and Compensation,” IEEE Transactions on Signal Processing, vol. 45, no. 10, pp. 2515–2532, October 1997.

    Google Scholar 

  21. J. Kirman, A. Nicholson, M. Lejter, T. Dean, and E. Santos Jr., “Using Goals to Find Plans with High Expected Utility,” in Proceedings of the Second European Workshop on Planning, 1993, pp. 158–170.

    Google Scholar 

  22. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, Englewood Cliffs, New Jersey, 1991.

    Google Scholar 

  23. B. Kosko, “Fuzzy Systems as Universal Approximators,” IEEE Transactions on Computers, vol. 43, no. 11, pp. 1329–1333, November 1994.

    Article  MATH  Google Scholar 

  24. B. Kosko, “Combining Fuzzy Systems,” in Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE FUZZ-95), March 1995, pp. 1855–1863.

    Google Scholar 

  25. B. Kosko, “Optimal Fuzzy Rules Cover Extrema,” International Journal of Intelligent Systems, vol. 10, no. 2, pp. 249–255, February 1995.

    Article  Google Scholar 

  26. B. Kosko, Fuzzy Engineering, Prentice Hall, 1996.

    Google Scholar 

  27. B. Kosko, Nanotime, Avon Books, 1997.

    Google Scholar 

  28. R. Krishnapuram and J. M. Keller, “A Possibilistic Approach to Clustering,” IEEE Transactions on Fuzzy Systems, vol. 1, pp. 98–110, May 1993.

    Article  Google Scholar 

  29. J. E. Laird, A. Newell, and P. S. Rosenbloom, “SOAR: An Architecture for General Intelligence,” Artificial Intelligence, vol. 33, pp. 1–64, 1987.

    Article  Google Scholar 

  30. P. Maes, “Agents that Reduce Work and Information Overload,” Communications of the ACM, vol. 37, no. 7, pp. 31–40, July 1994.

    Article  Google Scholar 

  31. P. Maes, “Artificial Life Meets Entertainment: Lifelike Autonomous Agents,” Communications of the ACM, vol. 38, no. 11, pp. 108–114, November 1995.

    Article  Google Scholar 

  32. P. Maes, “Modeling Adaptive Autonomous Agents,” in Artificial Life: An Overview, C. G. Langton, Ed., pp. 135–162. MIT Press, 1995.

    Google Scholar 

  33. P. Maes, T. Darrel, B. Blumberg, and A. Pentland, “The ALIVE System: Wireless, Full-body Interaction with Autonomous Agents,” Multimedia Systems, Spring 1996.

    Google Scholar 

  34. G. A. Miller, “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” The Psychological Review, vol. 63, no. 2, pp. 81–97, March 1956.

    Article  Google Scholar 

  35. I. Millet and P. T. Harker, “Globally Effective Questioning in the Analytic Hierarchy Process,” European Journal of Operational Research, vol. 48, pp. 88–97, 1990.

    Article  Google Scholar 

  36. S. Mitaim and B. Kosko, “What is the Best Shape for a Fuzzy Set in Function Approximation?,” in Proceedings of the 5th IEEE International Conference on Fuzzy Systems (FUZZ-96), September 1996, vol. 2, pp. 1237–1243.

    Article  Google Scholar 

  37. S. Mitaim and B. Kosko, “Neural Fuzzy Agents for Profile Learning and Adaptive Object Matching,” Presence: Special Issue on Autonomous Agents, Adaptive Behavior, and Distributed Simulations, vol. 7, no. 5, October 1998.

    Google Scholar 

  38. A. Moukas, “Amalthaea: Information Discovery and Filtering Using a Multiagent Evolving Ecosystem,” in Proceedings of the Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, April 1996.

    Google Scholar 

  39. T. Mullen and M. P. Wellman, “A Simple Computational Market for Network Information Services,” in Proceedings of the First International Conference on Multi-Agent Systems, June 1995, pp. 283–289.

    Google Scholar 

  40. A. Newell and_P. S. Rosenbloom, “Mechanisms of Skill Acquisition and the Law of Practice,” in Cognitive Skills and Their Acquisition, J. R. Anderson, Ed., chapter 1, pp. 1–55. Lawrence Erlbaum Associates, Inc., 1981.

    Google Scholar 

  41. W. Niblack, R. Barber, W. Equitz, M. Flikner, E. Glassman, D. Petkovic, P. Yanker, and C. Faloutsos, “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape,” Research Report RJ 9203 ( 81511 ), IBM, February 1993.

    Google Scholar 

  42. G. Owen, Game Theory, Academic Press, third edition, 1995.

    Google Scholar 

  43. N. R. Pal and J. C. Bezdek, “On Cluster Validity for the Fuzzy c-Means Model,” IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, pp. 370–379, August 1995.

    Article  Google Scholar 

  44. N. R. Pal, J. C. Bezdek, and R. J. Hathaway, “Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms,” Neural Networks, vol. 9, pp. 787–96, July 1996.

    Article  Google Scholar 

  45. S. Parsons and N. R. Jennings, “Negotiation through Argumentation—A Preliminary Report,” in Proceedings of the International Conference on Multi-Agent Systems, 1996.

    Google Scholar 

  46. A. Penland, R. W. Picard, and S. Sclaroff, “Photobook: Tools for Content-Based Manipulation of Image Databases,” in SPIE: Storage and Retrieval for Image and Video Database II, February 1994, vol. 2185, pp. 34–47.

    Article  Google Scholar 

  47. R. W. Picard and T. P. Minka, “Vision Texture for Annotation,” Multimedia Systems, vol. 3, pp. 3–14, 1995.

    Article  Google Scholar 

  48. W. K. Pratt, Digital Image Processing, Wiley Interscience, second edition, 1991.

    Google Scholar 

  49. D. W. Rasmus, “Intelligent Agents: DAI Goes to Work,” PC AI, pp. 27–32, January/February 1995.

    Google Scholar 

  50. W. S. Reilly and J. Bates, “Natural Negotiation for Believable Agents,” Technical Report CMU-CS-95–164, Carnegie Mellon University, Pittsburgh, PA, June 1995.

    Google Scholar 

  51. J. S. Rosenschein and G. Zlotkin, “Consenting Agents: Designing Conventions for Automated Negotiation,” AI Magazine, vol. 15, no. 3, pp. 29–46, Fall 1994.

    Google Scholar 

  52. T. L. Saaty, “A Scaling Method for Priorities in Hierarchical Structures,” Journal of Mathematical Psychology, vol. 15, pp. 234–281, 1977.

    Article  MathSciNet  MATH  Google Scholar 

  53. T. L. Saaty, “Axiomatic Foundation of the Analytic Hierarchy Process,” Management Science, vol. 32, no. 7, pp. 841–855, July 1986.

    Article  MathSciNet  MATH  Google Scholar 

  54. T. L. Saaty, “Highlights and Critical Points in the Theory and Application of the Analytic Hierarchy Process,” European Journal of Operational Research, vol. 74, pp. 426–447, 1994.

    Article  MATH  Google Scholar 

  55. T. Sandholm and V. Lesser, “Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework,” in Proceedings of the First International Conference on Multi-Agent Systems, June 1995, pp. 328–335.

    Google Scholar 

  56. E. Santos Jr. and D. O. Banks, “Acquiring Consistent Knowledge,” Technical Report AFIT/EN/TR96–01, Air Force Institute of Technology, January 1996.

    Google Scholar 

  57. L. Steels, “The Artificial Life Roots of Artificial Intelligence,” in Artificial Life: An Overview, C. G. Langton, Ed., pp. 75–110. MIT Press, 1995.

    Google Scholar 

  58. M. J. Swain and D. H. Ballard, “Color Indexing,” International Journal of Computer Vision, vol. 7, no. 1, pp. 11–32, 1991.

    Article  Google Scholar 

  59. M. Vetterli and J. Kovacevié, Wavelets and Subband Coding, Prentice Hall, 1995.

    Google Scholar 

  60. M. P. Wellman and J. Doyle, “Preferential Semantics for Goals,” in Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), July 1991, pp. 698–703.

    Google Scholar 

  61. J. K. Wu, A. D. Narasimhalu, B. M. Mehtre, and Y. J. Gao, “CORE: A Content-Based Retrieval Engine for Multimedia Information Systems,” Multimedia Systems, vol. 3, pp. 25–41, 1995.

    Article  Google Scholar 

  62. B. Yamauchi and R. Beer, “Integrating Reactive, Sequential, and Learning Behavior Using Dynamical Neural Networks,” in Proceedings of the Third International Conference on Simulation of Adaptive Behavior,D. Cliff, P. Husbands, J. A. Meyer, and S. Wilson, Eds. 1994, pp. 382–391, MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Signal and Image Processing Institute Department of Electrical Engineering—Systems, University of Southern California, Los Angeles, California, 90089-2564, USA

    Sanya Mitaim & Bart Kosko

Authors
  1. Sanya Mitaim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bart Kosko
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), Computer Science Division and Electronics Research Laboratory, Department of Electrical and Electronics, Engineering and Computer Science, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Mitaim, S., Kosko, B. (1999). Neural Fuzzy Intelligent Agents. In: Zadeh, L.A., Kacprzyk, J. (eds) Computing with Words in Information/Intelligent Systems 2. Studies in Fuzziness and Soft Computing, vol 34. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1872-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1872-7_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2461-2

  • Online ISBN: 978-3-7908-1872-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation