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Paraconsistent Annotated Logic Programs and Application to Intelligent Verification Systems

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Innovations in Intelligent Machines-4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 514))

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Abstract

Paraconsistent Logic is well known as a logical tool that can deal with inconsistency in a consistent logical system. In this chapter we introduce the development of a paraconsistent annotated logic program called Extended Vector Annotated Logic Program with Strong Negation (abbr. EVALPSN). First of all, we review paraconsistent annotated logics P T and their logic programs, and introduce the development from the paraconsistent logics P T to EVALPSN and their formal definitions. Furthermore, we introduce one version of EVALPSN named before-after (abbr. bf)-EVALPSN, which can deal with before-after relations between two time intervals, and its reasoning system consists of two kinds of inference rules with an example. We also provide a bf-EVALPSN based safety verification system for a pipeline process order control with simple examples.

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Notes

  1. 1.

    Generally the symbol T represents plural complete lattices of annotations, therefore, if there is no specification for the complete lattice T, assume that P T represents plural annotated logics in this chapter.

  2. 2.

    Hereafter expression ¬p:μ is conveniently used for expressing a negative annotated literal instead of ¬(p:μ) or p:¬(μ).

  3. 3.

    We use the right arrow symbol → in program clauses as a logical connective as far as no confusion occurs.

  4. 4.

    In this chapter, process Pr id is represented as p id in bf-EVALPSN literal.

  5. 5.

    If time t 1 is earlier than t 2, we conveniently denote the relation by inequality.

  6. 6.

    The bottom vector annotation (0,0) in transitive bf-inference rules implies that any vector annotation (i,j) satisfies it.

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Nakamatsu, K., Abe, J.M. (2014). Paraconsistent Annotated Logic Programs and Application to Intelligent Verification Systems. In: Faucher, C., Jain, L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-01866-9_9

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