Constructivist Machine Learning

Schmid, Thomas (2023) Constructivist Machine Learning. In: Compendium of Neurosymbolic Artificial Intelligence :. Frontiers in Artificial Intelligence and Applications . IOS Press. ISBN 9781643684062

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Abstract

While neuro-inspired and symbolic artficial intelligence have for a long time been con- sidered ideal complements, approaches to hybridize these concepts often lack an unifying grand theory. The way the philosophical concept of constructivism has been adapted for eductional purposes, however, provides a fruitful source of inspiration for this purpose. To this end, we have developed a framework termed Constructivist Machine Learning, which applies constructivist learning principles and exploits metadata on the grounds of Stachowiak’s General Model Theory in order to bridge the gap between neuro-spired and symbolic approaches. In this chapter, we summarize our previous work in order to introduce the reader to the most important ideas and concepts.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
?? artificial intelligence ??
ID Code:
202024
Deposited By:
Deposited On:
22 Sep 2023 10:10
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2024 05:20