First order abduction via tableau and sequent calculi

Mayer, Marta Cialdea; Pirri, Fiora

doi:10.1093/jigpal/1.1.99

Cited by 62 publications

(43 citation statements)

References 7 publications

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“…It has been used for abduction in (Nepomuceno, 2002;Reyes-Cabello et al, 2006), as an extension of the abductive approach originally presented in (Cialdea Mayer et al, 1993;Aliseda, 1997). In this paper, we define C-tableaux with respect to the set C of constants which defines a class of C-structures, rather than the cardinality of the searched models.…”

Section: The C-tableaux Calculusmentioning

confidence: 99%

Abduction viaC-tableaux and δ-resolution

Toscano

Nepomuceno–Fernández

Aliseda

2009

Journal of Applied Non-Classical Logics

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Section: The C-tableaux Calculusmentioning

confidence: 99%

Abduction viaC-tableaux and δ-resolution

Toscano

Nepomuceno–Fernández

Aliseda

2009

Journal of Applied Non-Classical Logics

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“…Various procedures for generation of abducibles have been proposed so far; some are designed for Classical Propositional Calculus (CPC for short) [1], others for more sophisticated propositional logics [29,26] or for first-order logic [16,25,28]. Those procedures, which are defined in a strictly logical setting, use, for example, the proof methods of analytic tableaux [1,26,25], of sequent calculi [26,25], or the dynamic proof method of adaptive logics [29,28].…”

Section: Introductionmentioning

confidence: 99%

“…Those procedures, which are defined in a strictly logical setting, use, for example, the proof methods of analytic tableaux [1,26,25], of sequent calculi [26,25], or the dynamic proof method of adaptive logics [29,28]. In this paper we will consider an implementation of a procedure generating abductive hypotheses for CPC.…”

Section: Introductionmentioning

confidence: 99%

Identifying Efficient Abductive Hypotheses Using Multicriteria Dominance Relation

Komosiński

Kups

Leszczyńska-Jasion

et al. 2014

ACM Trans. Comput. Logic

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In this article, results of the automation of an abductive procedure are reported. This work is a continuation of our earlier research [21], where a general scheme of the procedure has been proposed. Here, a more advanced system developed to generate and evaluate abductive hypotheses is introduced. Abductive hypotheses have been generated by the implementation of the Synthetic Tableau Method. Before the evaluation, the set of hypotheses has undergone several reduction phases. To assess usefulness of abductive hypotheses in the reduced set, several criteria have been employed. The evaluation of efficiency of the hypotheses has been provided by the multi-criteria dominance relation. To comprehend the abductive procedure and the evaluation process more extensively, analyses have been conducted on a number of artificially generated abductive problems.

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“…Abductive reasoning (also called abduction [11], abductive inference, [11] is a form of logical inference which starts with an observation then seeks to find the simplest and most likely explanation. Abduction was studied in computer science logic [17] both for propositional logic e.g., [16] and first order logic e.g., [15]. One of the most useful application of abduction in computer science is for logic programming [12].…”

Section: A Bird's Eye On Abductionmentioning

confidence: 99%

Abduction for Learning Smart City Rules

Bjørner¹,

Marinescu²,

Sagiv³

EPiC Series in Computing

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We propose using abduction for inferring implicit rules for Smart City ontologies. We show how we can use Z3 to extract candidate abducers from partial ontologies and leverage them in an iterative process of evolving an ontology by refining relations and restrictions, and populating relations. Our starting point is a Smart City initiative of the city of Barcelona, where a substantial ontology is being developed to support processes such as city planning, social services, or improving the quality of the data concerning (for instance) legal entities, whose incompleteness may sometimes hide fraudulent behavior. In our scenario we are supporting semantic queries over heterogeneous and noisy data. The approach we develop would allow evolving ontologies in an iterative fashion as new relations and restrictions are discovered.

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First order abduction via tableau and sequent calculi

Cited by 62 publications

References 7 publications

Abduction viaC-tableaux and δ-resolution

Abduction viaC-tableaux and δ-resolution

Identifying Efficient Abductive Hypotheses Using Multicriteria Dominance Relation

Abduction for Learning Smart City Rules

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