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Open peer commentary on the article “A Computational Constructivist Model as an Anticipatory Learning Mechanism for Coupled Agent–Environment Systems” by Filipo Studzinski Perotto. Upshot: Mainstream AI research largely addresses cognitive features as separate and unconnected. Instead of addressing cognitive growth in this same way – modeling it simply as one more such isolated feature and continuing to uphold a wrong-headed divide-and-conquer tradition – a constructivist approach should help unify many key phenomena such as anticipation, self-modeling, life-long learning, and recursive self-improvement. Since this is likely to result in complex systems with unanticipated properties, all cognitive architecture researchers should aim to implement their ideas in full as running systems to be verified by experiment. Perotto’s paper falls short on both these points.
Thórisson K. R. (2013) The power of constructivist ideas in artificial intelligence. Constructivist Foundations 9(1): 59–61. http://constructivist.info/9/1/059
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