Title :
Representing Causality Using Fuzzy Cognitive Maps
Author :
Mazlack, Lawrence J.
Author_Institution :
Appl. Comput. Intell. Lab., Univ. of Cincinnati, Cincinnati, OH, USA
Abstract :
Causal reasoning occupies a central position in human reasoning. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are severely limited in what portion of the common sense world they can represent. This paper considers the needs of commonsense causality and suggests Fuzzy Cognitive Maps as an alternative to DAGs.
Keywords :
cognitive systems; common-sense reasoning; data mining; directed graphs; fuzzy set theory; causal reasoning; causal relations; commonsense causality; directed acyclic graphs; fuzzy cognitive maps; human reasoning; Association rules; Computational intelligence; Data analysis; Data mining; Fuzzy cognitive maps; Fuzzy reasoning; Humans; Information processing; Laboratories; Marketing and sales; DAGs; causality; cognitive maps; commonsense; complexes; fuzzy; imprecision;
Conference_Titel :
Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American
Conference_Location :
Cincinnati, OH
Print_ISBN :
978-1-4244-4575-2
Electronic_ISBN :
978-1-4244-4577-6
DOI :
10.1109/NAFIPS.2009.5156434
