Ultimately-periodic Interval Model Checking for Temporal Dataset Evaluation

14 pages•Published: December 10, 2019

Dario Della Monica, Angelo Montanari, Aniello Murano and Guido Sciavicco

Abstract

Temporal dataset evaluation is the problem of establishing to what extent a set of temporal data (histories) complies with a given temporal condition. Checking interval temporal logic formulas against a finite model has been recently proposed, and proved successful, as a tool to solve such a problem. In this paper, we address the problem of checking interval temporal logic specifications, supporting interval length constraints, against infinite, finitely representable models, and we show the applicability of the resulting procedure to the evaluation of incomplete temporal datasets viewed as finite prefixes of ultimately-periodic histories.

Keyphrases: model checking, temporal dataset evaluation, ultimately periodic models

In: Diego Calvanese and Luca Iocchi (editors). GCAI 2019. Proceedings of the 5th Global Conference on Artificial Intelligence, vol 65, pages 28-41.

Links:	https://easychair.org/publications/paper/XS2V
	https://doi.org/10.29007/r3pf

BibTeX entry

@inproceedings{GCAI2019:Ultimately_periodic_Interval_Model,
  author    = {Dario Della Monica and Angelo Montanari and Aniello Murano and Guido Sciavicco},
  title     = {Ultimately-periodic Interval Model Checking for Temporal Dataset Evaluation},
  booktitle = {GCAI 2019. Proceedings of the 5th Global Conference on Artificial Intelligence},
  editor    = {Diego Calvanese and Luca Iocchi},
  series    = {EPiC Series in Computing},
  volume    = {65},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/XS2V},
  doi       = {10.29007/r3pf},
  pages     = {28-41},
  year      = {2019}}

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