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Reasoning About Surprising Scenarios in Description Logics of Typicality

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AI*IA 2016 Advances in Artificial Intelligence (AI*IA 2016)

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Abstract

We continue our investigation on nonmonotonic procedures for preferential Description Logics in order to reason about plausible but surprising scenarios. We consider an extension \(\mathcal {ALC}+\mathbf{T}_\mathbf{R}^{ \textsf {exp} }\) of the non-monotonic logic of typicality \(\mathcal {ALC}+\mathbf{T}_\mathbf{R}\) by inclusions of the form \(\mathbf{T}(C) \sqsubseteq _d D\), where d is a degree of expectedness. We consider a notion of extension of an ABox, in order to assume typicality assertions about individuals satisfying cardinality restrictions on concepts, then we define a preference relation among such extended ABoxes based on the degrees of expectedness, then we restrict entailment to those extensions that are minimal with respect to this preference relation. We propose a decision procedure for reasoning in \(\mathcal {ALC}+\mathbf{T}_\mathbf{R}^{ \textsf {exp} }\) and we exploit it to show that entailment is in ExpTime as for the underlying \(\mathcal {ALC}\). Last, we introduce a further extension of the proposed approach in order to reason about all plausible extensions of the ABox, by restricting the attention to specific degrees of expectedness ranging from the most surprising scenarios to the most expected ones.

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Notes

  1. 1.

    In Theorem 10 in [13] the authors have shown that for any KB there exists a finite minimal canonical model of KB minimally satisfying the ABox.

  2. 2.

    As mentioned, at this point of the presentation we only want to give an intuition of the inferences characterizing the logic \(\mathcal {ALC}+\mathbf{T}_\mathbf{R}^{ \textsf {exp} }\). Formal definitions of nonmonotonic entailment in \(\mathcal {ALC}+\mathbf{T}_\mathbf{R}^{ \textsf {exp} }\) will be provided in Definition 10.

  3. 3.

    Other aggregation functions could be used to define \(d_i\) (e.g. maximun/minimum degree). We aim at studying the impact of this choice on the reasoning machinery in future research.

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Acknowledgements

The author is partially supported by the project “ExceptionOWL: Nonmonotonic Extensions of Description Logics and OWL for defeasible inheritance with exceptions” by Università degli Studi di Torino and Compagnia di San Paolo, call 2014 “Excellent (young) PI”.

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Correspondence to Gian Luca Pozzato .

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Pozzato, G.L. (2016). Reasoning About Surprising Scenarios in Description Logics of Typicality. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_31

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  • DOI: https://doi.org/10.1007/978-3-319-49130-1_31

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