TY - CHAP

T1 - Constructivist Machine Learning

AU - Schmid, Thomas

PY - 2023/8/31

Y1 - 2023/8/31

N2 - 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.

AB - 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.

U2 - 10.3233/faia230138

DO - 10.3233/faia230138

M3 - Chapter (peer-reviewed)

SN - 9781643684062

T3 - Frontiers in Artificial Intelligence and Applications

BT - Compendium of Neurosymbolic Artificial Intelligence

A2 - Hitzler, P.

A2 - Sarker, M.K.

A2 - Eberhart, A.

PB - IOS Press

ER -