Abstract

In this paper we present PROLogic, a logic programming language based on a formal first-order fuzzy temporal logic: FTCLogic. FTCLogic integrates the advantages of a formal system (a first-order logic based on Possibilistic Logic) and an efficient mechanism with which to reason about time: the Fuzzy Temporal Constraints Networks or FTCN. PROLogic, therefore, is a Fuzzy Temporal PROLOG, which is implemented in Haskell.

Citation details of the article

Journal: International Journal of Applied Mathematics
Journal ISSN (Print): ISSN 1311-1728
Journal ISSN (Electronic): ISSN 1314-8060
Volume: 32
Issue: 4
Year: 2019

DOI: 10.12732/ijam.v32i4.10

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References

1. [1] J.A. Alonso Jim´enez, L´ogica en Haskell, In: https://www.cs.us.es/˜jalonso/publicaciones/2007-Logica en Haskell.pdf (2007), 113-134.
2. [2] S. Barro, R. Mar´ın, J. Mira and A.R. Pat´on, A model and a language for the fuzzy representation and handling of time, Fuzzy Sets and Systems, 61 (1994), 153-175.
3. [3] P. Blackburn, J. Bos and K. Striegnitz, Prolog Syntax, In: http://www.learnprolognow.org/lpnpage.php?pagetype=html &pageid=lpn-htmlse2 (2012).
4. [4] M. Campos, J.M. Juarez, J. Palma, R. Marn, and F. Palacios, Avian Influenza: Temporal modeling of a human to human transmission case, Expert Systems with Applications, 38 (2011), 8865-8885.
5. [5] M.A. C´ardenas-Viedma and R. Mar´ın, FTCLogic: Fuzzy temporal constraint logic, Fuzzy Set and Systems, 363 (2019), 84-112; DOI: 10.1016/j.fss.2018.05.014.
6. [6] R. Dechter, I. Meiri and J. Pearl, Temporal constraint networks, Artificial Intelligence, 49 (1991), 61-65.
7. [7] D. Dubois and H. Prade, Processing fuzzy temporal knowledge, IEEE Trans. on Systems, Man and Cybernetics, 19, No 4 (1989), 729-744.
8. [8] D. Dubois and H. Prade, Possibility tTheory and its applications: Where do we stand?, In: Springer Handbook Computational Intelligence, Eds. Janusz Kacprzuk and Witold Pedrycz (2015), 31-60, Springer Berlin Heidelberg.
9. [9] D. Dubois and H. Prade, Possibilistic logic - An overview, In: Handbook of the History of Logic. Volume 9: Computational Logic. J. Siekmann, Vol. Eds.; D.M. Gabbay, J. Woods, Series Eds. (2015), 283-342.
10. [10] S. Dutta, A Temporal Logic for uncertain events and an outline of a possible implementation in an extension of PROLOG, Proc.s of the Fourth Conf. on Uncertainty in Artificial Intelligence, UAI (1988).
11. [11] F.M. Galindo-Navarro and M.A. C´ardenas-Viedma, PROLogic, In: https://webs.um.es/mariancv/PROLogic/PROLogic.exe (2017).
12. [12] A. Kaufmann and M. Gupta, Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold, New York (1985).
13. [13] R. Marn, M.A. C´ardenas-Viedma, M. Balsa and J.L. S´anchez, Obtaining solutions in fuzzy constraint networks, Intern. J. of Approximate Reasoning 16, No 3-4 (1997), 261-288.
14. [14] S. Marlow, Happy User Guide. In: https://www.haskell.org/happy/doc/happy.pdf (2001).
15. [15] S. Marlow, Alex: A lexical analyser generator for Haskell, In: https://www.haskell.org/alex/ (2015).
16. [16] S. Marlow, Happy: The parser generator for haskell, In: https://www.haskell.org/happy/ (2015).
17. [17] E. Lamma, M. Milano and P. Mello, Extending constraint logic programming for temporal reasoning, Annals of Math. and Artificial Intelligence, 22, No 1-2 (1998), 139-158.
18. [18] E. Schwalb and L. Vila, Logic programming with temporal constraints, Proc. Third Intern. Workshop on Temporal Representation and Reasoning (TIME ’96), Key West, FL - USA (1996), 51-56.
19. [19] K. Ungchusak, P. Auewarakul, S. Dowell, R. Kiphati, W. Auwanit, P. Puthavathana et al., Probable person-to-person transmission of avian influenza A (H5N1), The New England J. of Medicine, 352 (2005), 333-340.