• DocumentCode
    2134551
  • Title

    Bayesian estimation vs fuzzy logic for heuristic reasoning

  • Author

    De Mathelin, Michel ; Perneel, Christiaan ; Acheroy, Marc

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    944
  • Abstract
    Bayesian estimation theory and fuzzy logic are used to derive knowledge combination rules for heuristic search algorithms. Such algorithms are typically used with expert systems. Heuristics are used to select candidate solutions or partial solutions of a complex problem whose solution space is too large to be fully explored. The information coming from the various heuristics and from observations made during the search must be combined. Combination and decision rules are first derived based on a probabilistic approach. Then, a fuzzy logic approach is followed and compared with the first approach
  • Keywords
    Bayes methods; fuzzy logic; heuristic programming; inference mechanisms; search problems; Bayesian estimation; candidate solutions; expert systems; fuzzy logic; heuristic reasoning; knowledge combination rules; partial solutions; probabilistic approach; solution space; Bayesian methods; Buildings; Estimation theory; Expert systems; Fuzzy logic; Heuristic algorithms; Military computing; Search methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 1993., Second IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0614-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.1993.327387
  • Filename
    327387