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. © 2019, EasyChair. All rights reserved.
Ultimately-periodic interval model checking for temporal dataset evaluation / Della Monica, D.; Montanari, A.; Murano, A.; Sciavicco, G.. - 65:(2019), pp. 28-41. (Intervento presentato al convegno 5th Global Conference on Artificial Intelligence, GCAI 2019) [10.29007/r3pf].
Ultimately-periodic interval model checking for temporal dataset evaluation
Della Monica, D.;Montanari, A.;Murano, A.;
2019
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. © 2019, EasyChair. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
