"Connectionism" is another different way of thinking in addition to "Symbolism."
Connectionism is based on a physiological finding that "the human brain is a circuit that utilizes the potential differences in neural elements called neurons," which became common in the late 19th and early 20th centuries. *1
In the 1950s, the combination of neurons of the brain was mathematically modeled as neural networks. Connectionism is an approach that tries to realize intelligence by neural networks. *2
Because neural networks are circuits that process a continuous signal, they do not understand symbols at all.
■References (Books, papers, Web articles, etc.)
Deep Learning (Adaptive Computation and Machine Learning series) Ian Goodfellow The MIT Press (November 18, 2016)
Artificial Intelligence: A Modern Approach, Global Edition (English Edition) 3rd Edition S.J.Russell, P.Norvig Pearson Education India; 3rd edition (October 14, 2015)
『マッチ箱の脳（AI）―使える人工知能のお話』 森川 幸人 二〇〇〇年
『人工知能のための哲学塾』 第一夜・コラム シンボリズムとコネクショニズム 三宅 陽一郎 二〇一六年
ゲームにおけるニューラルネットワーク「NERO における学習と進化 」（後半） 三宅陽一郎 https://www.slideshare.net/youichiromiyake/nero-51549529
*1: Hodgkin–Huxley model revealed the properties of electrical propagation of neurons. This work won a Nobel Prize in Physiology or Medicine.
*2: In the history of AI research, the term "Aritificial Intelligence (AI)" appeared for the first time at the Dartmouth workshop in 1956, the declaration of which already included neural networks as target.
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■References list of "AI meets Philosophy: how to design the Game AI"
