Combining Multiple Knowledge Representation Technologies into Agent Programming Languages

  • Mehdi M. Dastani
  • Koen V. Hindriks
  • Peter Novák
  • Nick A. M. Tinnemeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5397)


In most agent programming languages in practice a programmer is committed to the use of a single knowledge representation technology. In this paper we argue this is not necessarily so. It is shown that rational agent programming languages allow for the combination of various such technologies. Specific issues that have to be addressed to realize such integration for rational agents that derive their choice of action from their beliefs and goals are discussed. Two techniques to deal with these issues which enable the integration of multiple knowledge representation techniques are presented: a meaning-preserving translation approach that maps one representation to another, and an approach based on so-called bridge rules which add additional inference power to a system combining multiple knowledge representation technologies.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bordini, R., Hübner, J., Vieira, R.: Jason and the Golden Fleece of agent-oriented programming. In: Multi-Agent Programming - Languages, Platforms and Applications. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bordini, R.H., Dastani, M., Dix, J., Seghrouchni, A.E.F.: Multi-Agent Programming Languages, Platforms and Applications. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 15. Kluwer Academic Publishers, Dordrecht (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Trans. of KDE 1(1) (1989)Google Scholar
  4. 4.
    Dastani, M., Meyer, J.-J.C.: A Practical Agent Programming Language. In: Dastani, M., El Fallah Seghrouchni, A., Ricci, A., Winikoff, M. (eds.) ProMAS 2007. LNCS, vol. 4908, pp. 107–123. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Davis, R., Shrobe, H.E., Szolovits, P.: What is a knowledge representation? AI 14(1), 17–33 (1993)Google Scholar
  6. 6.
    de Boer, F., Hindriks, K., van der Hoek, W., Meyer, J.-J.: A Verification Framework for Agent Programming with Declarative Goals. Journal of Applied Logic (2007)Google Scholar
  7. 7.
    Dix, J., Zhang, Y.: IMPACT: A Multi-Agent Framework with Declarative Semantics. In: Multi-Agent Programming - Languages, Platforms and Applications. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Farquhar, A., Dappert, A., Fikes, R., Pratt, W.: Integrating Information Sources Using Context Logic. In: Knoblock, C., Levy, A. (eds.) Information Gathering from Heterogeneous, Distributed Environments (1995)Google Scholar
  9. 9.
    Giunchiglia, F., Serafini, L.: Multilanguage hierarchical logics or: How we can do without modal logics. AI 65(1), 29–70 (1994)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Levesque, H.: Foundations of a functional approach to knowledge representation. AI 23, 155–212 (1984)zbMATHGoogle Scholar
  11. 11.
    Makowsky, J.A., Ravve, E.V.: Translation schemes and the fundamental problem of database design. In: Thalheim, B. (ed.) ER 1996. LNCS, vol. 1157, pp. 5–26. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  12. 12.
    Minsky, M.: A framework for representing knowledge. In: Haughland, J. (ed.) Mind Design, pp. 95–128. MIT Press, Cambridge (1981)Google Scholar
  13. 13.
    Minsky, M.: The society of mind. Simon & Schuster, Inc., New York (1986)Google Scholar
  14. 14.
    Novák, P., Dix, J.: Modular BDI architecture. In: Nakashima, H., Wellman, M.P., Weiss, G., Stone, P. (eds.) Proc. AAMAS 2006, pp. 1009–1015. ACM, New York (2006)Google Scholar
  15. 15.
    Patil, R.S., Fikes, R.E., Patel-Schneider, P.F., McKay, D., Finin, T., Gruber, T.R., Neches, R.: The DARPA knowledge sharing effort: Progress report. In: Rich, C., Nebel, B., Swartout, W. (eds.) Princ. of KR and Reasoning: Proc. of the Third Int. Conf. Morgan Kaufmann, San Francisco (1992)Google Scholar
  16. 16.
    Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: A BDI Reasoning Engine. Multiagent Systems, Artificial Societies, and Simulated Organizations. In: [2], ch. 6, vol. 15, pp. 149–174 (2005)Google Scholar
  17. 17.
    Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. In: Proc. of the 2nd Int. Conf. on Princ. of KR and Reasoning, pp. 473–484 (1991)Google Scholar
  18. 18.
    Winikoff, M.: JACK(TM) Intelligent Agents: An Industrial Strength Platform. Multiagent Systems, Artificial Societies, and Simulated Organizations. In: [2], ch. 7, vol. 15, pp. 175–193 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mehdi M. Dastani
    • 1
  • Koen V. Hindriks
    • 2
  • Peter Novák
    • 3
  • Nick A. M. Tinnemeier
    • 1
  1. 1.Utrecht UniversityUtrechtThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.Clausthal University of TechnologyClausthal-ZellerfeldGermany

Personalised recommendations