文章基本信息

标题：適応アルゴリズム理解における認知バイアスの実験的検討
本地全文：下载
作者：寺田和憲 ; 山田誠二
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2019
卷号：34
期号：4
页码：1-9
DOI：10.1527/tjsai.A-I72
出版社：The Japanese Society for Artificial Intelligence
摘要：There have been few studies on cognitive bias for algorithm understanding in a human-computer cooperative situation. In the present study, we conducted an experiment with participants to investigate the cognitive process of higher level abstraction (algorithm understanding) performed in a human-computer collaboration task. The most recently used (MRU) algorithm, known to be one of the simplest adaptive algorithms, and probabilistic MRU algorithm were used to test the human capability to understand an algorithm. The experimental results showed that inductive reasoning in which participants observed the history of computer action, and they updated a statistical model while restricting their focus on a certain history with deterministic bias and Markov bias played key role to correctly understand the MRU algorithm. The results also showed that deductive reasoning was used to understand algorithms when participants rely on prior knowledge, and that there was a case in which the algorithm, even known to be the simplest one, was never understood.
关键词：algorithm understanding;inductive reasoning;deductive reasoning;adaptive user interface