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Fuzzy Causal Patterns of Humor and Jokes for Cognitive and Affective Computing

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  • Yingxu Wang

    (Department of Electrical and Computer Engineering, International Institute of Cognitive Informatics and Cognitive Computing, Alberta, Canada & Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Canada)

Abstract

Humor is an advanced emotional and cognitive ability of mankind that involves complex semantic inference and deep passionate appreciation. This paper presents the cognitive foundations of amusement and a general theory of humor based on the recent advances in cognitive informatics, cognitive linguistics, cognitive computing, and fuzzy causal analyses. A theory of fuzzy false causation (FFC) is introduced that reveals humor and jokes as false causations in fuzzy causal inferences. Base on the FFC theory, a general pattern of humor (GPH) is formalized for analyzing the settings and appreciations of a set of sample jokes. A formal measurement of the degree of amusement in jokes and humor is quantitatively described towards the rational explanation of jokes based on cognitive affective assessment. The formal models of humor and jokes enable machines for humor comprehension and appreciation in artificial intelligence, cognitive computing, computational intelligence, and cognitive robots.

Suggested Citation

  • Yingxu Wang, 2014. "Fuzzy Causal Patterns of Humor and Jokes for Cognitive and Affective Computing," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 8(2), pages 34-46, April.
    • Handle: RePEc:igg:jcini0:v:8:y:2014:i:2:p:34-46
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