logo

SCIENTIA SINICA Informationis, Volume 46 , Issue 11 : 1591-1607(2016) https://doi.org/10.1360/N112016-00108

Model checking generalized possibilistic computation tree logic based on decision processes

More info
  • ReceivedApr 24, 2016
  • AcceptedAug 2, 2016
  • PublishedNov 9, 2016

Abstract


Funded by

国家自然科学基金(11271237)

国家自然科学基金(61228305)

国家自然科学基金(61462001)

高等学校博士学科点专项科研基金(20130202110001)

高等学校博士学科点专项科研基金(20130202120002)


References

[1] Baier C, Katoen J P. Principles of Model Checking. Cambridge: MIT Press, 2008. Google Scholar

[2] Edmund M, Grumberg O, Peled D. Model Checking. Cambridge: MIT Press, 1999. Google Scholar

[3] Zheng Y, Wang L. Consensus of switched multi-agent systems. IEEE Trans Circ Syst II, 2016, 63: 314-318. Google Scholar

[4] Zheng Y, Wang L. A novel group consensus protocol for heterogeneous multi-agent systems. Int J Contr, 2015, 106: 1-13. Google Scholar

[5] Li T, Zhang J F. Consensus conditions of multi-agent systems with time-varying topologies and stochastic communication noises. IEEE Trans Automat Contr, 2010, 55: 2043-2057 CrossRef Google Scholar

[6] Li T, Fu M, Xie L, et al. Distributed consensus with limited communication data rate. IEEE Trans Automat Contr, 2011, 56: 279-292 CrossRef Google Scholar

[7] Baier C, Kwiatkowska M. Model checking for a probabilistic branching time logic with fairness. Distrib Comput, 1998, 11: 125-155 CrossRef Google Scholar

[8] Hart S, Sharir M. Termination of probabilistic concurrent programs. ACM Trans Prog Lang Syst, 1983, 5: 356-380 CrossRef Google Scholar

[9] Sultan K, Bentahar J, Wei W, et al. Modeling and verifying probabilistic multi-agent systems using knowledge and social commitments. Expert Syst Appl, 2014, 41: 6291-6304 CrossRef Google Scholar

[10] Sultan K, Bentahar J, EI-Menshawy M. Model checking probabilistic social commitments for intelligent agent communication. Appl Softw Comput, 2014, 22: 397-409 CrossRef Google Scholar

[11] Chechik M, Devereux B, Easterbrook S, et al. Multi-valued symbolic model-checking. ACM Trans Softw Eng Method, 2003, 12: 371-408 CrossRef Google Scholar

[12] Chechik M, Gurfinkel A, Devereux B, et al. Data structures for symbolic multi-valued model-checking. Formal Methods Syst Des, 2006, 29: 295-344 CrossRef Google Scholar

[13] Pan H Y, Li Y M, Cao Y Z, et al. Model checking fuzzy computation tree logic. Fuzzy Sets Syst, 2015, 262: 60-77 CrossRef Google Scholar

[14] Pan H Y, Li Y M, Cao Y Z, et al. Model checking computation tree logic over finite lattices. Theor Comput Sci, 2016, 612: 45-62 CrossRef Google Scholar

[15] Li Y M, Li L J. Model checking of linear-time properties based on possibility measure. IEEE Trans Fuzzy Syst, 2013, 21: 842-854 CrossRef Google Scholar

[16] Li Y M, Li Y L, Ma Z Y. Computation tree logic model checking based on possibility measures. Fuzzy Sets Syst, 2015, 262: 44-59 CrossRef Google Scholar

[17] Li Y M, Ma Z Y. Quantitative computation tree logic model checking based on generalized possibility measures. IEEE Trans Fuzzy Syst, 2015, 23: 2034-2047 CrossRef Google Scholar

[18] Drakopoulos A. Probabilities, possibilities, and fuzzy sets. Fuzzy Sets Syst, 1995, 75: 1-15 CrossRef Google Scholar

[19] Dubois D. Possibility theory and statistical reasoning. Comput Stat Data Anal, 2006, 51: 47-69 CrossRef Google Scholar

[20] Dubois D, Prade H. Possibility Theory. NewYork: Plenum, 1988. Google Scholar

[21] Dubois D, Prade H. Possibility theory, probability theory and multiple-valued logics: a clarification. Ann Math Artif Intell, 2001, 32: 35-66 CrossRef Google Scholar

[22] Grabisch M, Murofushi T, Sugeno M. Fuzzy Measures and Integrals. Heidelberg: Physica-Verlag, 2000. Google Scholar

[23] Zadeh L A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst, 1978, 1: 3-28 CrossRef Google Scholar

[24] Li Y M. Analysis of Fuzzy Systems. Beijing: Science Press, 2005 [李永明. 模糊系统分析. 北京: 科学出版社, 2005]. Google Scholar

[25] Garmendia L, Gonz$\acute{a}$lez del Campo R, López V, et al. An algorithm to compute the transitive closure, a transitive approximation and a transitive opening of a fuzzy proximity. Mathware Soft Comput, 2009, 16: 175-191. Google Scholar

[26] Lin F, Ying H. Modeling and control of fuzzy discrete event systems. IEEE Trans Syst Man Cybernetics Part B, 2002, 32: 408-415 CrossRef Google Scholar

[27] Qiu D. Supervisory control of fuzzy discrete event systems: a formal approach. IEEE Trans Syst Man Cybern Part B, 2005, 35: 72-88 CrossRef Google Scholar

[28] Cao Y, Ying M. Observability and decentralized control of fuzzy discrete-event systems. IEEE Trans Fuzzy Syst, 2006, 14: 202-216 CrossRef Google Scholar

[29] Liu F C, Qiu D W. Diagnosability of fuzzy discrete event systems: a fuzzy approach. IEEE Trans Fuzzy Syst, 2009, 17: 372-384 CrossRef Google Scholar

[30] Xing H Y, Zhang Q S, Huang K S. Analysis and control of fuzzy discrete event systems using bisimulation equivalence. Theor Comput Sci, 2012, 456: 100-111 CrossRef Google Scholar