other · Lancaster EPrints (Lancaster University) · 2023

Constructivist Machine Learning

Schmid, Thomas

Cited by 0Abstract and details only

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.

Cite this paper

APA 7

Schmid, T. (2023). Constructivist Machine Learning. Lancaster EPrints (Lancaster University).

MLA 9

Schmid, Thomas. “Constructivist Machine Learning.” Lancaster EPrints (Lancaster University), 2023.

Chicago

Schmid, Thomas. 2023. “Constructivist Machine Learning.” Lancaster EPrints (Lancaster University).

Harvard

Schmid, T. (2023) ‘Constructivist Machine Learning’, Lancaster EPrints (Lancaster University).

BibTeX

@misc{schmid2023constructivist,
  title = {{Constructivist Machine Learning}},
  author = {Schmid, Thomas},
  journal = {Lancaster EPrints (Lancaster University)},
  year = {2023}
}

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