Symbolic artificial intelligence: Difference between revisions

Revision as of 00:45, 16 November 2024

Revision as of 00:14, 25 November 2024

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* Neural|Symbolic—uses a neural architecture to interpret perceptual data as symbols and relationships that are then reasoned about symbolically.

* Neural:Symbolic → Neural—relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model, e.g., to train a neural model for symbolic computation by using a [[Macsyma]]-like symbolic mathematics system to create or label examples.

* Neural_{Symbolic}—uses a neural net that is generated from symbolic rules. An example is the Neural Theorem Prover,<ref>{{Cite conference| publisher = Association for Computational Linguistics| doi = 10.18653/v1/W16-1309| pages = 45–50| last1 = Rocktäschel| first1 = Tim| last2 = Riedel| first2 = Sebastian| title = Learning Knowledge Base Inference with Neural Theorem Provers| book-title = Proceedings of the 5th Workshop on Automated Knowledge Base Construction| ___location = San Diego, CA| accessdate = 2022-08-06| date = 2016| url = https://aclanthology.org/W16-1309| doi-access = free}}</ref> which constructs a neural network from an [[And–or tree|AND-ORAND–OR]] proof tree generated from knowledge base rules and terms. Logic Tensor Networks<ref>{{Citation| arxiv = 1606.04422| last1 = Serafini| first1 = Luciano| last2 = Garcez| first2 = Artur d'Avila| title = Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge| date = 2016}}</ref> also fall into this category.

* Neural[Symbolic]—allows a neural model to directly call a symbolic reasoning engine, e.g., to perform an action or evaluate a state.