Human behavioral modeling using fuzzy and Dempster-Shafer theory

被引:7
|
作者
Yager, Ronald R. [1 ]
机构
[1] Iona Coll, Inst Machine Intelligence, New Rochelle, NY 10801 USA
关键词
D O I
10.1007/978-0-387-77672-9_11
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Human behavioral modeling requires an ability to represent and manipulate imprecise cognitive concepts. It also needs to include the uncertainty and unpredictability of human action. We discuss the appropriateness of fuzzy sets for representing human centered cognitive concepts. We describe the technology of fuzzy systems modeling and indicate its the role in human behavioral modeling. We next introduce some ideas from the Dempster-Shafer theory of evidence. We use the Dempster-Shafer theory to provide a machinery for including randomness in the fuzzy systems modeling process. This combined methodology provides a framework with which we can construct models that can include both the complex cognitive concepts and unpredictability needed to model human behavior.
引用
收藏
页码:89 / 99
页数:11
相关论文
共 50 条
  • [41] Integrated Data Fusion Using Dempster-Shafer Theory
    Zhang, Yang
    Zeng, Qing-An
    Liu, Yun
    Shen, Bo
    2015 FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE THEORY, SYSTEMS AND APPLICATIONS (CCITSA 2015), 2015, : 98 - 103
  • [42] Dempster-Shafer Theory and Connections to Information Theory
    Peri, Joseph S. J.
    SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XXII, 2013, 8745
  • [43] MEASURE OF STRIFE IN DEMPSTER-SHAFER THEORY
    VEJNAROVA, J
    KLIR, GJ
    INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 1993, 22 (01) : 25 - 42
  • [44] Dempster-Shafer Theory for Stock Selection
    Salehy, Nima
    Okten, Giray
    2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021), 2021, : 1729 - 1734
  • [45] Particle filtering in the Dempster-Shafer theory
    Reineking, Thomas
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2011, 52 (08) : 1124 - 1135
  • [46] Handling of Dependence in Dempster-Shafer Theory
    Su, Xiaoyan
    Mahadevan, Sankaran
    Xu, Peida
    Deng, Yong
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2015, 30 (04) : 441 - 467
  • [47] An extended approach for Dempster-Shafer theory
    Campos, F
    Cavalcante, S
    PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2003, : 338 - 344
  • [48] MEASURES OF DISCORD IN THE DEMPSTER-SHAFER THEORY
    RAMER, A
    KLIR, G
    INFORMATION SCIENCES, 1993, 67 (1-2) : 35 - 50
  • [49] The Evidential Reasoning Approach to Medical Diagnosis using Intuitionistic Fuzzy Dempster-Shafer Theory
    Wang, Yanni
    Dai, Yaping
    Chen, Yu-wang
    Meng, Fancheng
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2015, 8 (01) : 75 - 94
  • [50] A neural model for fuzzy Dempster-Shafer classifiers
    Binaghi, E
    Gallo, I
    Madella, P
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2000, 25 (02) : 89 - 121