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
Link To Document :
بازگشت