Skip to main content
SpringerLink
Log in

Neural Network Approach for Learning of the World Structure by Cognitive Agents

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

  • 1499 Accesses

Abstract

In this work an original method for coping with agents’ incomplete knowledge is introduced. This method called the algorithm for the messages generation is applied by the cognitive agents when the states of external objects can not be directly perceived. To approximate the current states of objects all agent’s experience as temporal data base is taken into account. As a result of the algorithm the logic formulas with modal operators are generated. One of the steps of proposed algorithm is the classification of the observations. It is shown how neural network approach might be used in order to determined some tendencies to occurrence specific states of objects.

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 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Barreto, G.A.: Identification and Control of Dynamical Systems using the Self-Organizing Map. IEEE Trans. Neural Networks 15, 1244–1259 (2004)

    Article  Google Scholar 

  2. Coradeschi, S., Saffiotti, A.: An Introduction to the Anchoring Problem. Robotics and Autonomous Systems 43, 85–96 (2003)

    Article  Google Scholar 

  3. Daszykowski, M., Walczak, W., Massart, D.L.: On the Optimal Partitioning of Data with K-Means, Growing K-Means, Neural Gas, and Growing Neural Gas. J. Chem. Inf. Comput. Sci. 42, 1378–1389 (2002)

    Google Scholar 

  4. Egghe, L., Michel, C.: Construction of weak and strong similarity measures for ordered sets of documents using fuzzy set techniques. Information Processing and Management 39, 771–807 (2003)

    Article  MATH  Google Scholar 

  5. Harnad, S.: The Symbol Grounding Problem. Physica 42, 335–236

    Google Scholar 

  6. Katarzyniak, R., Pieczynska-Kuchtiak, A.: Formal Modelling of the Semantics for Communication Languages in Systems of Believable Agents. In: Proc. of ISAT 2001, Szklarska Poreba, pp. 174–181 (2001)

    Google Scholar 

  7. Katarzyniak, R., Pieczynska-Kuchtiak, A.: Intentional Semantics for Logic Disjunctions, Alternatives and Cognitive agent’s Belief. In: Proc. of the 14th International Conference on System Science, Wroclaw, Poland, pp. 370–382 (2001)

    Google Scholar 

  8. Katarzyniak, R., Pieczynska-Kuchtiak, A.: A Consensus Based Algorithm for Grounding Belief formulas in Internally Stored Perceptions. Neural Network World 5, 671–682 (2002)

    Google Scholar 

  9. Katarzyniak, R., Pieczynska-Kuchtiak, A.: Distance Measure Between Cognitive Agent’s Stored Perceptions. In: Proc. of IASTED International Conference on Modelling, Identyfication and Control, MIC 2002, Innsbruck, Austria, pp. 517–522 (2002)

    Google Scholar 

  10. Katarzyniak, R., Pieczynska-Kuchtiak, A.: Grounding Languages in Cognitive Agents and Robots. In: Proc. of Sixteenth International Conference on System Engineering, Coventry, pp. 332–337 (2003)

    Google Scholar 

  11. Katarzyniak, R., Pieczynska-Kuchtiak, A.: Grounding and extracting modal responses in cognitive agents: AND query and states of incomplete knowledge. International Journal of Applied Mathematics and Computer Science 14(2), 249–263 (2004)

    MATH  MathSciNet  Google Scholar 

  12. Katarzyniak, R., Pieczynska-Kuchtiak, A.: An Approach to resolving semantic inconsistency of multiple prepositional attitudes. Journal of Intelligent & Fuzzy Systems 17(3) (to appear, 2006)

    Google Scholar 

  13. Ossowski, S.: Neural Networks – algorithmic approach (in polish), WNT Warsaw (1996)

    Google Scholar 

  14. Nguyen, N.T.: Consensus System for Solving Conflicts in Distributed Systems. Information Sciences 147, 91–122 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  15. Pieczyńska-Kuchtiak, A.: A Decision Function in the Algorithm for the Choice of Semantic Messages. In: Proc. of Information Systems Architecture and Technology, Poland (2002)

    Google Scholar 

  16. Pieczynska-Kuchtiak, A.: Towards measure of semantic correlation between messages in multiagent system. In: ICCS 2005, LNCS, Kraków, pp. 567–574 (2004)

    Google Scholar 

  17. Vogt, P.: Anchoring of semiotics symbols. Robotics and Autonomous Systems 43, 109–120 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Science and Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Agnieszka Pieczyńska & Jarosław Drapała

Authors
  1. Agnieszka Pieczyńska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jarosław Drapała
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pieczyńska, A., Drapała, J. (2006). Neural Network Approach for Learning of the World Structure by Cognitive Agents. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_128

Download citation

  • DOI: https://doi.org/10.1007/11893011_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation