Conflicts and disagreements are associated with issues related to living beings in particular with humans. Certain steps towards resolving these issues help to minimize conflict. Very few researchers paid their attention towards this important topic to develop some techniques which are based on mathematical methods. Rough set theory as a new and powerful mathematical tool to handle uncertainty in decision making problems was used to study conflict analysis and decision making. Afterwards the Pawlak conflict analysis model was established. Subsequently Deja put forward some questions which are not answered by Pawlak conflict analysis model. In the present paper firstly, we introduce the notions of soft preference relation and soft dominance relation and analyzed the Middle East conflict. Secondly, we answered the questions posed by Deja. Thirdly, two new techniques of reduction of parameters/attributes are introduced and applied to the Middle East conflict.
AliM.I., FengF., LiuX.Y., MinW.K. and ShabirM., On some new operations in soft set theory, Comput Math Appl57 (2009), 1547–1553.
2.
AliM.I., A note on soft sets, rough sets and fuzzy soft sets, Appl Soft Comput11 (2011), 3329–3332.
3.
AliM.I., ShabirM. and NazM., Algebraic structures of soft sets associated with new operations, Comput Math Appl61 (2011), 2647–2654.
4.
AliM.I., Another view on reduction of parameters in soft sets, Appl Soft Comput12 (2012), 1814–1821.
5.
AlcantudJ.C.R., de Andres CalleR. and MariaJ.M., Torrecillas, Hesitant fuzzy worth: An innovative ranking methodology for hesitant fuzzy subsets, Appl Soft Comput38 (2016), 232–243.
6.
AlcantudJ.C.R. and TorraV., Decomposition theorems and extension principles for hesitant fuzzy sets, Inform Fusion41 (2018), 48–56.
7.
BazanJ., SkowronA. and SynakP., Dynamic reducts as a tool for extracting laws from decision tables, In: Proceedings of the 8th International Symposium ISMIS’94 on Methodologies for Intelligent Systems, Charlotte, NG, LNAI 869, Springer, Berlin, 1994, pp. 346–355.
8.
BraszczynskiJ., GrecoS. and SlowinskiR., Multi-criteria classification a new scheme for application of dominance-based decision rules, Eur J Oper Res181 (2007), 1030–1044.
9.
CagmanN. and EnginoS., Soft matrix theory and its decisionmaking, Comput Math Appl59 (2010), 3308–3314.
10.
CagmanN. and EnginoS., Soft set theory and uni-int decisionmaking, Eur J Oper Res207 (2010), 848–855.
11.
DashM. and LiuH., Feature selection for classification, Intell Data Anal1(3) (1997). (on-line journal) http://wwweast.elsevier.com/ida
12.
DejaR., Conflict model with negotiation, Bull Polish Acad Sci Tec Sci44(4) (1996), 475–498.
13.
DejaR., Conflict analysis, Int J Intell syst17 (2002), 235–253.
14.
DuboisD., FargierH. and PradeH., Ordinal and probabilistic representations of acceptance, J Artif Intell Res22 (2004), 23–56.
15.
GauW.L. and BuehrerD.J., Vague sets, IEEE Trans Sys Man Cyber23 (1993), 610–614.
16.
JiangY., LiuH., TangaY. and ChenQ., Semantic decision-making using ontology based soft sets, Math Comput Model53 (2011), 1140–1149.
17.
KryszkiewiczM. and RybinskiH., Finding reducts in composed information systems, Fund Inform2(2–3) (1996), 183–196.
18.
MajiP.K., BiswasR. and RoyA.R., Soft set theory, Comput Math Appl45 (2003), 555–562.
19.
MaX., ZhanJ., AliM.I. and MehmoodN., A survey of decision making methods based on two classes of hybrid soft set models, Artif Intell Rev (2016). DOI 10.1007/s10462-016-9534-2
20.
MaX., LiuQ. and ZhanJ., A survey of decision making methods based on certain hybrid soft set models, Artif Intell Rev47 (2017), 507–530.
21.
MolodtsovD., Soft set theory-First results, Comput Math Appl37 (1999), 19–31.
22.
MoshkovichH.M., MechitovA.I. and OlsonD.L., Rule induction in data mining: Effect of ordinal scales, Expert Syst Appl22 (2002), 303–311.
23.
NguyenN.T., Consensus system for solving conflicts in distributed system, Inform Sci1-4(147) (2002), 91–122.
24.
OrlowskaM. and OrlowskiM., Maintenance of knowledge in dynamic information systems, in: Slowinski (Ed.), Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht, 1992, pp. 315–330.
25.
PawlakZ., Rough set, Int J Comput Inform Sci11 (1982), 341–356.
26.
PawlakZ., Rough Sets, Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht, 1991.
27.
PawlakZ., An inquiry into anatomy of conflicts, J Inform Sci109 (1998), 65–68.
28.
RoyA.R. and MajiP.K., A fuzzy soft set theoretic approach to decision making problems, J Comput Appl Math203 (2007), 412–418.
29.
RodriguezR., MartinezL., TorraV., XuZ. and HerreraF., Hesitant fuzzy sets: State of the art and future directions, Int J Intell Syst29 (2014), 495–524.
30.
SlowinskiR., GrecoS. and MatarazzoB., Rough-Set-Based Decision support. Search Methodologies, Springer, 2014, pp. 557–607.
31.
SunB., MaW. and ZhaoH., Rough set based conflict analysis model and method over two universes, Inform Sci372 (2016), 111–125.
32.
TorraV., Hesitant fuzzy sets, Int J Intell Syst25(6) (2010), 529–539.
ZhanJ. and ZhuK., Reviews on decision making methods on fuzzy soft sets and rough soft sets, J Intell Fuzzy Syst29 (2015), 1169–1176.
35.
ZhanJ., YuB. and FoteaV.E., Characterizations of two kinds of hemiring based on probability spaces, Soft Comput20 (2016), 637–648.
36.
ZhanJ., LiuQ. and ZhuW., Another approach to rough soft hemirings and corresponding decision making, Soft Comput21 (2017), 3769–3780.
37.
ZhanJ., LiuQ. and HerawanT., A novel soft rough set: Soft rough hemirings and its multicriteria group decion making, Appl Soft Comput54 (2017), 393–402.
38.
ZhanJ. and ZhuK., A novel soft rough fuzzy set: Z-soft rough fuzzy ideals of hemirings and corresponding decision making, Soft Comput21 (2017), 1923–1936.
39.
ZhanJ., AliM.I. and MehmoodN., On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methodes, Appl Soft Comput56 (2017), 446–457.
