Analyzing the Impact of Characteristics on Artificial Intelligence IQ Test: A Fuzzy Cognitive Map Approach | Zendy

Fangyao Liu | Zendy; Yuejin Zhang | Zendy; Yong Shi | Zendy; Zhengxin Chen | Zendy; Xixi Feng | Zendy

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Analyzing the Impact of Characteristics on Artificial Intelligence IQ Test: A Fuzzy Cognitive Map Approach

Author(s) -

Fangyao Liu,

Yuejin Zhang,

Yong Shi,

Zhengxin Chen,

Xixi Feng

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.10.221

Subject(s) - defuzzification , computer science , fuzzy logic , artificial intelligence , fuzzy cognitive map , enhanced data rates for gsm evolution , membership function , cognition , data mining , machine learning , fuzzy set , fuzzy number , neuroscience , biology

This research paper we present a Fuzzy Cognitive Map (FCM)-based approach to improving a previously proposed IQ test for Artificial Intelligence (AI) systems. Starting from linguistic terms analyses, fuzzy logic along with triangular membership function is adopted for the defuzzification process. Based on the defuzzification result, a calculated defuzzified value is assigned for the quantitative weights of each edge in the resulting FCM. Mean Square Error (MSE) is used for evaluation. Experiments have shown that the FCM-based approach outperforms other methods (including Delphi weights).

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