Knowledge representation and reasoning: Difference between revisions

Revision as of 06:37, 17 April 2025

Revision as of 06:39, 17 April 2025

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In a broader sense, parameterized mechanisms of knowledge representation — including [[Neural network (machine learning)|neural network]] architectures such as [[Convolutional neural network|convolutional neural networks]] and [[Transformer (deep learning architecture)|transformers]] — can also be regarded as a family of knowledge representation formalisms. The question of which formalism is most appropriate for knowledge-based systems has long been a subject of extensive debate. For instance, Frank van Harmelen et al. discussed the suitability of logic as a knowledge representation formalism and reviewed arguments presented by anti-logicists.<ref>{{Cite book |last1=Porter |first1=Bruce |title=Handbook of knowledge representation |last2=Lifschitz |first2=Vladimir |last3=Van Harmelen |first3=Frank |date=2008 |publisher=Elsevier |isbn=978-0-444-52211-5 |edition=1st |series=Foundations of artificial intelligence |___location=Amsterdam Boston}}</ref> Paul Smolensky criticized the limitations of symbolic formalisms and explored the possibilities of integrating it with connectionist approaches.<ref>{{Cite journal |last=Smolensky |first=Paul |date=March 1988 |title=On the proper treatment of connectionism |url=https://www.cambridge.org/core/product/identifier/S0140525X00052432/type/journal_article |journal=Behavioral and Brain Sciences |language=en |volume=11 |issue=1 |pages=1–23 |doi=10.1017/S0140525X00052432 |issn=0140-525X}}</ref>

More recently, Heng Zhang and his colleagues have demonstrated that all universal (or equally expressive and natural) knowledge representation formalisms are recursively isomorphic. The authors suggest that this isomorphism implies an essential equivalence among mainstream knowledge representation formalisms with respect to their capacity for supporting [[artificial general intelligence]] (AGI). They further argue that while diverse technical approaches may draw insights from one another via recursive isomorphisms, the fundamental challenges remain inherently shared.<ref>{{Cite journal |last1=Zhang |first1=Heng |last2=Jiang |first2=Guifei |last3=Quan |first3=Donghui |date=2025-04-11 |title=A Theory of Formalisms for Representing Knowledge |url=https://ojs.aaai.org/index.php/AAAI/article/view/33674 |journal=Proceedings of the AAAI Conference on Artificial Intelligence |language=en |volume=39 |issue=14 |pages=15257–15264 |doi=10.1609/aaai.v39i14.33674 |issn=2374-3468}}</ref> The authors suggest that this isomorphism implies an essential equivalence among mainstream knowledge representation formalisms with respect to their capacity for supporting [[artificial general intelligence]] (AGI). They further argue that while diverse technical approaches may draw insights from one another via recursive isomorphisms, the fundamental challenges remain inherently shared.

== History ==