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
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