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Annotated Logic Applications for Imperfect Information

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Abstract

Imperfect information is a very general term that comprises different types of information, such as uncertain, vague, fuzzy, inconsistent, possibilistic, probabilistic, partially or totally incomplete information [2]. In the literature of knowledge representation we find a different formal model for each one of these distinct types. For example, annotated logic is a formal model to represent inconsistent information.

Annotated logics are non-classical logics introduced in [20] as a logic programming theory. They were proved to be paraconsistent. Based on [5], we present in this work the annotated logic programming theory and some of its applications in Artificial Intelligence (AI). We present it as a formalism to reason with inconsistent information and investigate its possibility to represent other types of imperfect information, such as possibilistic and non-monotonic reasoning. Our main goal is to verify and confirm the importance of annotated logics as a tool for developing knowledge-based and automated reasoning systems in AI.

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Author notes

  1. Daniela V. Carbogim

    Present address: Department of Artificial Intelligence, University of Edinburgh, 80 South Bridge, Edinburgh, Scotland, EH1 1HN

Authors and Affiliations

  1. Instituto de Matemática e Estatística, Universidade de São Paulo Rua do Matão 1010, São, Paulo-SP, Brasil, 05508-900

    Flávio S. Corrêa da Silva

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  1. Daniela V. Carbogim
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  2. Flávio S. Corrêa da Silva
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Carbogim, D.V., da Silva, F.S.C. Annotated Logic Applications for Imperfect Information. Applied Intelligence 9, 163–172 (1998). https://doi.org/10.1023/A:1008268003741

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