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Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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Logic-based argumentation with existential rules
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In: ISSN: 0888-613X ; International Journal of Approximate Reasoning ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01596666 ; International Journal of Approximate Reasoning, Elsevier, 2017, 90, pp.76-106. ⟨10.1016/j.ijar.2017.07.004⟩ (2017)
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An empirical evaluation of argumentation in explaining inconsistency tolerant query answering
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In: 30th International Workshop on Description Logics ; DL: Description Logics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01586155 ; DL: Description Logics, Jul 2017, Montpellier, France ; https://project.inria.fr/dl2017/ (2017)
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Explanatory dialogues with argumentative faculties over inconsistent knowledge bases ; Dialogue Explicatif et Argumentation sur des Bases de Connaissances Incohérentes
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01596665 ; Expert Systems with Applications, Elsevier, 2017, 80, pp.244-262. ⟨10.1016/j.eswa.2017.03.009⟩ (2017)
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Extending GWAPs for Building Profile Aware Associative Networks
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In: 22nd International Conferences on Conceptual Structures (ICCS 2016) ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01328671 ; 22nd International Conferences on Conceptual Structures (ICCS 2016), Jul 2016, Annecy, France. pp.43-58, ⟨10.1007/978-3-319-40985-6_4⟩ ; https://www.irit.fr/ICCS2016/ (2016)
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Key Discovery for Numerical Data: Application to Oenological Practices
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In: 22th International Conference on Graph-Based Representation and Reasoning ; ICCS: International Conference on Conceptual Structures ; https://hal.archives-ouvertes.fr/hal-01837440 ; ICCS: International Conference on Conceptual Structures, Jul 2016, Annecy, France. pp.222-236, ⟨10.1007/978-3-319-40985-6_17⟩ ; https://www.irit.fr/ICCS2016/ (2016)
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| 7 |
A Dialectical Proof Theory for Universal Acceptance in Coherent Logic-based Argumentation Frameworks
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In: 22nd European Conference on Artificial Intelligence ; ECAI : European Conference on Artificial Intelligence ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01333368 ; ECAI : European Conference on Artificial Intelligence, Aug 2016, The Hague, Netherlands. pp.55-63, ⟨10.3233/978-1-61499-672-9-55⟩ ; http://www.ecai2016.org/ (2016)
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| 8 |
Dialectical Characterization of Consistent Query Explanation with Existential Rules
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In: 29th International Florida Artificial Intelligence Research Society Conference ; FLAIRS: Florida Artificial Intelligence Research Society ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01328679 ; FLAIRS: Florida Artificial Intelligence Research Society, May 2016, Key Largo, United States ; http://www.flairs-29.info (2016)
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DALEK: a Tool for Dialectical Explanations in Inconsistent Knowledge Bases
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In: 6th International Conference on Computational Models of Argument ; COMMA: Computational Models of Argument ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01337019 ; COMMA: Computational Models of Argument, Sep 2016, Potsdam, Germany. pp.461-462, ⟨10.3233/978-1-61499-686-6-461⟩ ; http://www.ling.uni-potsdam.de/comma2016/ (2016)
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| 10 |
Formalizing Explanatory Dialogues
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In: 9th International Conference on Scalable Uncertainty Management ; SUM: Scalable Uncertainty Management ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01164705 ; SUM: Scalable Uncertainty Management, Sep 2015, Quebec City, Canada. pp.282-297, ⟨10.1007/978-3-319-23540-0_19⟩ ; http://www.fernuni-hagen.de/wbs/sum2015/ (2015)
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| 11 |
What Can Argumentation Do for Inconsistent Ontology Query Answering?
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In: https://hal-lirmm.ccsd.cnrs.fr/lirmm-00812630 ; RR-13007, 2013, pp.17 (2013)
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Abstract:
The area of inconsistent ontological knowledge base query answering studies the problem of inferring from an inconsistent ontology. To deal with such a situation, different semantics have been defined in the literature (e.g. AR, IAR, ICR). Argumentation theory can also be used to draw conclusions under inconsistency. Given a set of arguments and attacks between them, one applies a particular semantics (e.g. stable, preferred, grounded) to calculate the sets of accepted arguments and conclusions. However, it is not clear what are the similarities and differences of semantics from ontological knowledge base query answering and semantics from argumentation theory. This paper provides the answer to that question. Namely, we prove that: (1) sceptical acceptance under stable and preferred semantics corresponds to ICR semantics; (2) universal acceptance under stable and preferred semantics corresponds to AR semantics; (3) acceptance under grounded semantics corresponds to IAR semantics. We also prove that the argumentation framework we define satisfies the rationality postulates (e.g. consistency, closure).
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-00812630/document
https://hal-lirmm.ccsd.cnrs.fr/lirmm-00812630/file/TR-Vesic-Croitoru.pdf
https://hal-lirmm.ccsd.cnrs.fr/lirmm-00812630
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| 12 |
Conceptual Structures: From Information to Intelligence
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In: https://hal-lirmm.ccsd.cnrs.fr/lirmm-00538396 ; Madalina Croitoru, Sébastien Ferré, Dickson Lukose. Springer, 6208, 207 p., 2010, Lecture Notes in Computer Science, 978-3-642-14196-6 ; http://hal-lirmm.ccsd.cnrs.fr/lirmm-00538396 (2010)
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