Advertisement

Consistency Verification of the Reasoning in a Deliberative Agent with Respect to the Communication Protocols

  • Jaime Ramírez
  • Angélica de Antonio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3963)

Abstract

The paper presents a method that can detect inconsistencies in the reasoning carried out by a deliberative agent in a changing environment. The verified agent operates on a description of the world represented by means of an OWL Lite ontology, and utilizes production rules to take decisions related to its future behaviour. The considered kind of rules allows for representing non-monotonic reasoning and linear arithmetic constraints in the rule antecedents. The proposed method can specify the scenarios in which the agent would deduce an inconsistency. A scenario is defined to be a description of the initial agent’s state (in the agent life cycle), a deductive tree of rule firings, and a partially ordered set of messages and/or stimuli schemas that the agent must receive from other agents and/or the environment. Besides, the method will make sure that the scenarios will be valid w.r.t. the communication protocols in which the agent is involved.

Keywords

Object Property Temporal Constraint Integrity Constraint Knowledge Base System Datatype Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    de Kleer, J.: An assumption based TMS. Artificial Intelligence 28, 127–162 (1986)CrossRefGoogle Scholar
  2. 2.
    Rousset, M.: On the consistency of knowledge bases: The COVADIS system. In: Proceedings ECAI 1988, Munich, Alemania, pp. 79–84 (1988)Google Scholar
  3. 3.
    Ginsberg, A.: Knowledge-base reduction: A new approach to checking knowledge bases for inconsistency and redundancy. In: Proceedings of the AAAI 1988, pp. 585–589 (1988)Google Scholar
  4. 4.
    Meseguer, P.: Incremental verification of rule-based expert systems. In: Proceedings of the 10th European Conference on AI (ECAI 1992), pp. 840–844 (1992)Google Scholar
  5. 5.
    Dahl, M., Williamson, K.: A verification strategy for long-term maintenance of large rule-based systems. In: Workshop Notes of the AAAI 1992 WorkShop on Verification and Validation of expert Systems, pp. 66–71 (1992)Google Scholar
  6. 6.
    Ayel, M., Laurent, J.P.: Validation, Verification and Test of Knowledge-Based Systems: SACCO-SYCOJET: Two Different Ways of Verifying Knowledged-Based Systems. John Wiley publishers, Chichester (1991)Google Scholar
  7. 7.
    Horrocks, I., Patel-Schneider, P.F.: Three theses of representation in the semantic web. In: Proc. of the Twelfth International World Wide Web Conference (WWW 2003), pp. 39–47. ACM, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Baader, F., Nutt, W.: Basic description logics. In: [19], ch. 2 pp. 43–95 (2003)Google Scholar
  9. 9.
    Baader, F., et al.: The Description Logic Handbook. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  10. 10.
    Ramírez, J., de Antonio, A.: Knowledge base semantic verification based on contexts propagation. In: Notes of the AAAI 2001 Symposium on Model-based Validation of Intelligence (2001), http://ase.arc.nasa.gov/mvi/abstracts/index.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jaime Ramírez
    • 1
  • Angélica de Antonio
    • 1
  1. 1.Technical University of MadridMadridSpain

Personalised recommendations