Skip to main content
SpringerLink
Log in

Common understanding in a multi-agent system using ontology-guided learning

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agents represent their knowledge more independently and give them more flexibility, it also adds to the complexity of communication. We believe that agents can overcome this complexity by using their learning capability. The agents can learn any concept they do not know but want to communicate about with other agents in the multi-agent system where they work in. Our goal in this paper is to present a general method for agents using ontologies to teach each other concepts to improve their communication, and therefore cooperation abilities. In our method, a particular agent that understands a concept only ambiguously intends to learn it by receiving positive and negative examples for that concept from the other agents. Then, utilizing one of the known concept learning methods, the agent learns the concept in question. In case of conflicts in the received set of examples, the learning agent asks other agents again to get involved in the learning process by taking votes. While this method allows agents not to share common ontologies, it enables agents to establish common grounds on the concepts known only by some of them if these common grounds are needed during cooperation. In fact, the learned concepts by an agent are compromised among the views of other agents the method improves the autonomy of agents using them significantly.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. We keep the basic assumptions of the original algorithm unchanged. These assumptions include division by \((m.k)\) in line 7 and definition of \(\phi \) in line 10.

    figure a1

References

  1. Afsharchi M, Far BH, Denzinger J (2009) Enhancing communication with groups of agents using learned non-unanimous ontology concepts. Int J Web Intell Agent Syst 7(1):107–121

    Google Scholar 

  2. Afsharchi M, Far BH, Denzinger J (2006) Ontology guided learning to improve communication among groups of agents. In: Proceedings of the 5th international joint conference on autonomous agents and multi agent systems (AAMAS’06), Hakodate, pp 923–930

  3. Alexopoulos P, Wallace M, Kafentzis K, Askounis D (2011) IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones. Knowl Inf Syst 1–29

  4. Buckley C, Salton G, Allan J (1994) The effect of adding relevance information in a relevance feedback environment. In: Proceedings of the 17th annual international conference on research and development in information retrieval(SIGIR94). Springer, New York, pp 292–300

  5. Çensoy M (2009) Concept learning for achieving personalized ontologies: an active learning approach vol 5680. ADMI, Springer, New York

    Google Scholar 

  6. Finin T, Labrou Y, Mayfied J (1997) Software agents. In: Bradshaw J (ed) KQML as an agent communication language. MIT Press, Cambridge

    Google Scholar 

  7. Illinois Semantic Integration Archive. http://anhai.cs.uiuc.edu/archive/, (as seen on Jan 30, 2005)

  8. Jurisica I, Mylopoulos J, Yu E (2004) Ontologies for knowledge management: an information systems perspective. Knowl Inf Syst 6:380–401

    Article  Google Scholar 

  9. Jim K-C, Giles CL (2000) Talking helps: evolving communicating agents for the predator-prey pursuit problem. Artif Life 6(3):237–254

    Article  Google Scholar 

  10. JATLite ftp site. ftp://java.stanford.edu/JATLite/

  11. JENA: A Semantic Web Framework for Java. http://jena.sourceforge.net/, (as seen on Jan 30, 2005)

  12. Koller D, Sahami M (1997) Hierarchically classifying documents using very few words. In: Proceedings of the ICML-97, pp 170–178

  13. Leite M (2012) Relating ontologies with a fuzzy information model. Knowl Inf Syst 1–33

  14. Mitchell TM (1997) Machine learning. McGraw-Hill, New York

    MATH  Google Scholar 

  15. Mirbakhsh N, Didandeh A, Afsharchi M (2009) Incremental non-unanimous concept reformation through queried object classification. In: Proceedings of the international conference on intelligent agent technology (IAT09), Italy

  16. OWL:Web Ontology Language. http://www.w3.org/TR/owl-features/, (as seen on Sep 20, 2004)

  17. Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11(3):387–434

    Article  Google Scholar 

  18. Pinto HS, Martins JP (2004) Ontologies: how can they be built? Knowl Inf Syst 6:441–464

    Article  Google Scholar 

  19. Packer H, Payne T, Gibbins N, Jennings NR (2008) Evolving ontological knowledge bases through agent collaboration. In: Proceedings of the 6th European workshop on multi-agent systems

  20. Protege: An Open Source Ontology Editor and Knowledge-base Framework. http://protege.stanford.edu/, (as seen on Jan 30, 2005)

  21. Rocchio JJ (1971) Relevance feedback in information retrieval, The SMART retrieval system, experiments in automatic document processing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  22. Robnik-ikonja M, Kononenko I (2003) Theoretical and empirical analysis of relieff and rrelieff. Mach Learn 53:23–69

    Article  Google Scholar 

  23. Sen S, Kar PP (2002) Sharing a concept. AAAI Tech Report SS-02-02, Stanford

  24. Steels L (1998) The origins of ontologies and communication conventions in multi-agent systems. Auton Agents Multi-Agent Syst 1(2):169–194

    Article  Google Scholar 

  25. Stumme G (2002) Using ontologies and formal concept analysis for organizing business knowledge. In: Becker J, Knackstedt R (eds) Wissensmanagement mit Referenzmodellen—Konzepte für die Anwendungssystem- und Organisationsgestaltung. Physica, pp 163–174

  26. University of Michigan academic units. http://www.umich.edu/units.html, (as seen on Jan 30, 2005)

  27. van Diggelen J, Beun RJ, Dignum F, van Eijk RM, Meyer JJ (2007) Ontology negotiation: goals, requirements and implementation. Int J Agent-Oriented Softw Eng 1(1):63–90

    Article  Google Scholar 

  28. van Diggelen J, Beun RJ, Dignum F, van Eijk RM, Meyer J-JCh (2006) ANEMONE: an effective minimal ontology negotiation environment. Proc AAMAS 2006:899–906

    Article  Google Scholar 

  29. Williams AB (2004) Learning to share meaning in a multi agent system. Auton Agents Multi Agent Syst 8(2):165–193

    Article  Google Scholar 

  30. Williams AB, Padmanabhan A, Blake MB (2003) Local consensus ontologies for B2B-oriented service discovery. In: Proceedings of the AAMAS-03, pp 647–654

  31. Zhang J, Kang D-K, Silvescu A, Honavar V (2006) Learning accurate and concise Naive Bayes classifiers from attribute value taxonomies and data. Knowl Inf Syst 9(2):157–179

    Article  Google Scholar 

  32. Zhu X, Ding W, Yu PS, Zhang C (2010) One-class learning and concept summarization for data streams. Knowl Inf Syst 1-31-31

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran

    Mohsen Afsharchi

  2. Department of Computer Science, Western University Canada, London, Canada

    Arman Didandeh & Nima Mirbakhsh

  3. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz H. Far

Authors
  1. Mohsen Afsharchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arman Didandeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nima Mirbakhsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behrouz H. Far
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arman Didandeh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Afsharchi, M., Didandeh, A., Mirbakhsh, N. et al. Common understanding in a multi-agent system using ontology-guided learning. Knowl Inf Syst 36, 83–120 (2013). https://doi.org/10.1007/s10115-012-0524-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-012-0524-7

Keywords

Search

Navigation