Measuring Instability in Normal Residuated Logic Programs: Discarding Information

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Authors

Nicolás Madrid

Published

1 January 2010

Publication details

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods - 13th International Conference, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, Part {I} , Communications in Computer and Information Science vol. 80, pages 128–137.

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Abstract

Citation

Please, cite this work as:

[MO10] N. Madrid and M. Ojeda-Aciego. “Measuring Instability in Normal Residuated Logic Programs: Discarding Information”. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods - 13th International Conference, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, Part I. Ed. by E. Hüllermeier, R. Kruse and F. Hoffmann. Vol. 80. Communications in Computer and Information Science. Springer, 2010, pp. 128-137. DOI: 10.1007/978-3-642-14055-6_14. URL: https://doi.org/10.1007/978-3-642-14055-6_14.

BibTeX

@InProceedings{Madrid2010a,
     author = {Nicol{’a}s Madrid and Manuel Ojeda-Aciego},
     booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods - 13th International Conference, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, Part {I}},
     title = {Measuring Instability in Normal Residuated Logic Programs: Discarding Information},
     year = {2010},
     editor = {Eyke H{"u}llermeier and Rudolf Kruse and Frank Hoffmann},
     pages = {128–137},
     publisher = {Springer},
     series = {Communications in Computer and Information Science},
     volume = {80},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/conf/ipmu/MadridO10.bib},
     doi = {10.1007/978-3-642-14055-6_14},
     timestamp = {Mon, 03 Jan 2022 00:00:00 +0100},
     url = {https://doi.org/10.1007/978-3-642-14055-6_14},
}

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Measuring Instability in Normal Residuated Logic Programs: Discarding Information

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

[1] M. E. Cornejo, D. Lobo, and J. Medina. “Measuring the Incoherent Information in Multi-adjoint Normal Logic Programs”. In: Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, Sep. 2017, p. 521–533. ISBN: 9783319668307. DOI: 10.1007/978-3-319-66830-7_47. URL: http://dx.doi.org/10.1007/978-3-319-66830-7_47.

[2] M. E. Cornejo, D. Lobo, and J. Medina. “Selecting the Coherence Notion in Multi-adjoint Normal Logic Programming”. In: Advances in Computational Intelligence. Springer International Publishing, 2017, p. 447–457. ISBN: 9783319591537. DOI: 10.1007/978-3-319-59153-7_39. URL: http://dx.doi.org/10.1007/978-3-319-59153-7_39.

[3] J. Janssen, S. Schockaert, D. Vermeir, et al. “A core language for fuzzy answer set programming”. In: International Journal of Approximate Reasoning 53.4 (Jun. 2012), p. 660–692. ISSN: 0888-613X. DOI: 10.1016/j.ijar.2012.01.005. URL: http://dx.doi.org/10.1016/j.ijar.2012.01.005.

[4] J. Janssen, S. Schockaert, D. Vermeir, et al. “Aggregated Fuzzy Answer Set Programming”. In: Annals of Mathematics and Artificial Intelligence 63.2 (Aug. 2011), p. 103–147. ISSN: 1573-7470. DOI: 10.1007/s10472-011-9256-8. URL: http://dx.doi.org/10.1007/s10472-011-9256-8.

[5] N. Madrid and M. Ojeda-Aciego. “Measuring Inconsistency in Fuzzy Answer Set Semantics”. In: IEEE Transactions on Fuzzy Systems 19.4 (Aug. 2011), p. 605–622. ISSN: 1941-0034. DOI: 10.1109/tfuzz.2011.2114669. URL: http://dx.doi.org/10.1109/tfuzz.2011.2114669.

[6] N. Madrid and M. Ojeda-Aciego. “Measuring instability in normal residuated logic programs: Adding information”. In: International Conference on Fuzzy Systems. IEEE, Jul. 2010, p. 1–7. DOI: 10.1109/fuzzy.2010.5584819. URL: http://dx.doi.org/10.1109/fuzzy.2010.5584819.

[7] N. Madrid and M. Ojeda-Aciego. “On the measure of incoherent information in extended multi-adjoint logic programs”. In: 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE, Apr. 2013, p. 30–37. DOI: 10.1109/foci.2013.6602452. URL: http://dx.doi.org/10.1109/foci.2013.6602452.

[8] N. Madrid and M. Ojeda-Aciego. “On the use of fuzzy stable models for inconsistent classical logic programs”. In: 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE, Apr. 2011, p. 115–121. DOI: 10.1109/foci.2011.5949476. URL: http://dx.doi.org/10.1109/foci.2011.5949476.

[9] M. Ulbricht, M. Thimm, and G. Brewka. “Handling and measuring inconsistency in non-monotonic logics”. In: Artificial Intelligence 286 (Sep. 2020), p. 103344. ISSN: 0004-3702. DOI: 10.1016/j.artint.2020.103344. URL: http://dx.doi.org/10.1016/j.artint.2020.103344.

[10] M. Ulbricht, M. Thimm, and G. Brewka. “Measuring Inconsistency in Answer Set Programs”. In: Logics in Artificial Intelligence. Springer International Publishing, 2016, p. 577–583. ISBN: 9783319487588. DOI: 10.1007/978-3-319-48758-8_42. URL: http://dx.doi.org/10.1007/978-3-319-48758-8_42.