Automated Verification of Resource Requirements in Multi-Agent Systems Using Abstraction

  • Natasha Alechina
  • Brian Logan
  • Hoang Nga Nguyen
  • Abdur Rakib
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6572)


We describe a framework for the automated verification of multi-agent systems which do distributed problem solving, e.g. query answering. Each reasoner uses facts, messages and Horn clause rules to derive new information. We show how to verify correctness of distributed problem solving under resource constraints, such as the time required to answer queries and the number of messages exchanged by the agents. The framework allows the use of abstract specifications consisting of Linear Time Temporal Logic (LTL) formulas to specify some of the agents in the system. We illustrate the use of the framework on a simple example.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bordini, R.H., Hübner, J.F., Vieira, R.: Jason and the Golden Fleece of agent-oriented programming. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming: Languages, Platforms and Applications. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bordini, R., Fisher, M., Visser, W., Wooldridge, M.: State-space reduction techniques in agent verification. In: Jennings, N.R., Sierra, C., Sonenberg, L., Tambe, M. (eds.) Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004), pp. 896–903. ACM Press, New York (2004)Google Scholar
  3. 3.
    Adjiman, P., Chatalic, P., Goasdoué, F., Rousset, M.C., Simon, L.: Distributed reasoning in a peer-to-peer setting. In: López de Mántaras, R., Saitta, L. (eds.) Proceedings of the Sixteenth European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, pp. 945–946. IOS Press, Amsterdam (2004)Google Scholar
  4. 4.
    Claßen, J., Eyerich, P., Lakemeyer, G., Nebel, B.: Towards an integration of Golog and planning. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI 2007), pp. 1846–1851. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  5. 5.
    Alechina, N., Jago, M., Logan, B.: Modal logics for communicating rule-based agents. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (eds.) Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006), pp. 322–326. IOS Press, Amsterdam (2006)Google Scholar
  6. 6.
    Alur, R., Henzinger, T.A., Mang, F.Y.C., Qadeer, S., Rajamani, S.K., Tasiran, S.: MOCHA: Modularity in model checking. In: Y. Vardi, M. (ed.) CAV 1998. LNCS, vol. 1427, pp. 521–525. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Hirtle, D., Boley, H., Grosof, B., Kifer, M., Sintek, M., Tabet, S., Wagner, G.: Schema Specification of RuleML 0.91 (2006),
  8. 8.
    Clavel, M., Eker, S., Lincoln, P., Meseguer, J.: Principles of maude. Electr. Notes Theor. Comput. Sci. 4 (1996)Google Scholar
  9. 9.
    Alechina, N., Logan, B., Nga, N.H., Rakib, A.: Verifying time and communication costs of rule-based reasoners. In: Peled, D., Wooldridge, M. (eds.) MoChArt 2008. LNCS, vol. 5348, pp. 1–14. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Holzmann, G.J.: On-the-fly model checking. ACM Computing Surveys 28(4) (1996)Google Scholar
  11. 11.
    Clarke, E.M., Grumberg, O., Long, D.E.: Model checking and abstraction. In: Proceedings of the 19th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp. 342–354 (1992)Google Scholar
  12. 12.
    Cousot, P., Cousot, R.: Abstract interpretation: A unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th Annual ACM Symposium on Principles of Programming Languages, pp. 238–252 (1977)Google Scholar
  13. 13.
    Culbert., C.: CLIPS reference manual. NASA (2007)Google Scholar
  14. 14.
    Friedman-Hill., E.J.: Jess, The Rule Engine for the Java Platform. Sandia National Laboratories (2008)Google Scholar
  15. 15.
    Tzafestas, S., Ata-Doss, S., Papakonstantinou, G.: Knowledge-Base System Diagnosis, Supervision and Control. Plenum Press, New York (1989)CrossRefGoogle Scholar
  16. 16.
    Chen, J.R., Cheng, A.M.K.: Predicting the response time of OPS5-style production systems. In: Proceedings of the 11th Conference on Artificial Intelligence for Applications, p. 203. IEEE Computer Society, Los Alamitos (1995)CrossRefGoogle Scholar
  17. 17.
    Cheng, A.M.K., yen Tsai, H.: A graph-based approach for timing analysis and refinement of OPS5 knowledge-based systems. IEEE Transactions on Knowledge and Data Engineering 16(2), 271–288 (2004)CrossRefGoogle Scholar
  18. 18.
    Brodsky, A., Sagiv, Y.: On termination of Datalog programs. In: International Conference on Deductive and Object-Oriented Databases (DOOD), pp. 47–64 (1989)Google Scholar
  19. 19.
    Alpuente, M., Feliu, M.A., Joubert, C., Villanueva, A.: Defining datalog in rewriting logic. In: De Schreye, D. (ed.) LOPSTR 2009. LNCS, vol. 6037, pp. 188–204. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Natasha Alechina
    • 1
  • Brian Logan
    • 1
  • Hoang Nga Nguyen
    • 1
  • Abdur Rakib
    • 1
  1. 1.University of NottinghamUK

Personalised recommendations