Publication: Adding a Degree of Certainty to Deductions in a Fuzzy Temporal Constraint Prolog: FTCProlog
Authors
Cárdenas-Viedma, María-Antonia
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Publisher
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DOI
. https://doi.org/10.3390/axioms13070472
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info:eu-repo/semantics/article
Description
© 2024 by the author. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published, version of a Published Work that appeared in final form in Axioms. To access the final edited and published work see https://doi.org/10.3390/axioms13070472
Abstract
The management of time is essential in most AI-related applications. In addition, we know
that temporal information is often not precise. In fact, in most cases, it is necessary to deal with
imprecision and/or uncertainty. On the other hand, there is the need to handle the implicit commonsense information present in many temporal statements. In this paper, we present FTCProlog, a logic
programming language capable of handling fuzzy temporal constraints soundly and efficiently. The
main difference of FTCProlog with respect to its predecessor, PROLogic, is its ability to associate
a certainty index with deductions obtained through SLD-resolution. This resolution is based on
a proposal within the theoretical logical framework FTCLogic. This model integrates a first-order
logic based on possibilistic logic with the Fuzzy Temporal Constraint Networks (FTCNs) that allow
efficient time management. The calculation of the certainty index can be useful in applications
where one wants to verify the extent to which the times elapsed between certain events follow a
given temporal pattern. In this paper, we demonstrate that the calculation of this index respects the
properties of the theoretical model regarding its semantics. FTCProlog is implemented in Haskell.
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Citation
Axioms 2024, 13, 472
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