• DocumentCode
    2642466
  • Title

    Commonsense imprecise satisficing causal complexes

  • Author

    Mazlack, Lawrence J.

  • Author_Institution
    Appl. Artificial Intelligence Lab., Cincinnati Univ., OH, USA
  • fYear
    2005
  • fDate
    26-28 June 2005
  • Firstpage
    389
  • Lastpage
    394
  • Abstract
    Causality is imprecisely granular in many ways. Knowledge of at least some causal effects is inherently imprecise. Complete knowledge of all possible factors might lead to a crisp causal understanding. However, it is unlikely that all possible factors can be known for many subjects; consequently, causal knowledge is inherently incomplete and therefore imprecise. Causal complexes are groupings of smaller causal relations that make up a large grained causal object . Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many fine-grained events. However, the larger-grained causal objects are necessarily more imprecise as some of their constituent components. A satisficing solution might be to develop large-grained solutions and then only go to the finer-grain when the impreciseness of the large-grain is unsatisfactory.
  • Keywords
    causality; cause-effect analysis; common-sense reasoning; causal complex; causal effect; causal knowledge; commonsense reasoning; Artificial intelligence; Automobiles; Decision making; Glass; Humans; Laboratories; Legged locomotion; Physics computing; Psychology; Road accidents;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
  • Print_ISBN
    0-7803-9187-X
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2005.1548567
  • Filename
    1548567