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Inconsistency Management for Traffic Regulations: Formalization and Complexity Results

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Logics in Artificial Intelligence (JELIA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7519))

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Abstract

Smart Cities is a vision driven by the availability of governmental data that fosters many challenging applications. One of them is the management of inconsistent traffic regulations, i.e., the handling of inconsistent traffic signs and measures in urban areas such as wrong sign posting, or errors in data acquisition in traffic sign administration software. We investigate such inconsistent traffic scenarios and formally model traffic regulations using a logic-based approach for traffic signs and measures, and logical theories describe emerging conflicts on a graph-based street model. Founded on this model, we consider major reasoning tasks including consistency testing, diagnosis, and repair, and we analyze their computational complexity for different logical representation formalisms. Our results provide a basis for an ongoing implementation of the approach.

Supported by PRISMA solutions EDV-Dienstleistungen GmbH, and the Austrian Science Fund (FWF) projects P20841 and P24090.

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References

  1. Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Representation results for defeasible logic. ACM Trans. Comput. Logic 2(2), 255–287 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  2. Brewka, G., Eiter, T., Truszczyński, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)

    Article  Google Scholar 

  3. Console, L., Torasso, P.: Automated diagnosis. Intelligenza Artificiale 3(1-2), 42–48 (2006)

    Google Scholar 

  4. Dantsin, E., Eiter, T., Gottlob, G., Voronkov, A.: Complexity and Expressive Power of Logic Programming. ACM Comput. Surv. 33(3), 374–425 (2001)

    Article  Google Scholar 

  5. Eiter, T., Faber, W., Fink, M., Woltran, S.: Complexity Results for Answer Set Programming with Bounded Predicate Arities. Ann. Math. Artif. Intell. 51(2-4), 123–165 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Eiter, T., Gottlob, G., Leone, N.: Abduction From Logic Programs: Semantics and Complexity. Theoretical Computer Science 189(1-2), 129–177 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  7. Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer set programming. In: IJCAI, pp. 90–96 (2005)

    Google Scholar 

  8. Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: The Potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)

    MathSciNet  MATH  Google Scholar 

  9. Gelfond, M., Lifschitz, V.: Classical Negation in Logic Programs and Disjunctive Databases. Next Generat. Comput. 9(3-4), 365–386 (1991)

    Article  MATH  Google Scholar 

  10. de Kleer, J., Kurien, J.: Fundamentals of model-based diagnosis. In: IFAC Symposium SAFEPROCESS 2003, pp. 25–36. Elsevier (2003)

    Google Scholar 

  11. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV System for Knowledge Representation and Reasoning. ACM TOCL 7(3), 499–562 (2006)

    Article  MathSciNet  Google Scholar 

  12. Lucas, P.: Symbolic diagnosis and its formalisation. Knowl. Eng. Rev. 12, 109–146 (1997)

    Article  Google Scholar 

  13. Maher, M.J.: Propositional defeasible logic has linear complexity. Theory Pract. Log. Program. 1(6), 691–711 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  14. Papadimitriou, C.H.: Computational Complexity. Addison-Wesley (1994)

    Google Scholar 

  15. Poole, D.: Normality and faults in logic-based diagnosis. In: IJCAI, pp. 1304–1310 (1989)

    Google Scholar 

  16. Poole, D.: Representing diagnosis knowledge. Ann. Math. Artif. Intell. 11, 33–50 (1994)

    Article  MATH  Google Scholar 

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Beck, H., Eiter, T., Krennwallner, T. (2012). Inconsistency Management for Traffic Regulations: Formalization and Complexity Results. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds) Logics in Artificial Intelligence. JELIA 2012. Lecture Notes in Computer Science(), vol 7519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33353-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-33353-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33352-1

  • Online ISBN: 978-3-642-33353-8

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