Knowledge representation and reasoning: Difference between revisions

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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, 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> 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) formalisms of knowledge representation formalisms are recursively isomorphic,. whichThe indicatesauthors suggest that this isomorphism implies an essential equivalence among mainstream knowledge representation formalisms are essentially equivalent inwith termsrespect ofto their capacity for realizingsupporting [[artificial general intelligence]] (AGI);. They further argue that while diverse technical approaches canmay draw insights from one another via recursive isomorphisms, yet the fundamental challenges areremain 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>

== History ==