Show simple item record

dc.contributor.authorCai, Cheng-Hao
dc.contributor.authorKe, Dengfeng
dc.contributor.authorXu, Yanyan
dc.contributor.authorSu, Kaile
dc.date.accessioned2017-12-04T12:00:31Z
dc.date.available2017-12-04T12:00:31Z
dc.date.issued2017
dc.identifier.doi10.1109/IJCNN.2017.7966113
dc.identifier.urihttp://hdl.handle.net/10072/355078
dc.description.abstractSymbolic reasoning is difficult for neural networks. Especially, reasoning with variables can be a challenging task for them. In this paper, a symbolic reasoning method based on deep neural networks is proposed, and this method is applied to axiom discovery. This method makes use of the concept of “symbolic manipulation”. Specifically, it relies on the learning ability of the deep neural networks and the reasoning ability of a logical system: The logical system generates training examples, which indicate how to manipulate symbols, from given data, and then the deep neural networks try to learn these examples, score them and abstract possible axioms from them. In particular, this method enables the deep neural networks to realise simple reasoning with variables in predicate logic. In experiments, we demonstrate that the deep neural networks are able to learn to copy and generate symbols from a certain form of rules produced by the logical system. Moreover, we find that the more hidden layers usually mean the stronger learning ability of symbolic manipulation: An increasing number of hidden layers usually bring about a higher rule acceptance rate. Also, we find that the more hidden layers can bring about better results on axiom discovery tasks, and we show that the deep neural networks can discover some useful axioms in mathematics.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIJCNN 2017
dc.relation.ispartofconferencetitle2017 International Joint Conference on Neural Networks (IJCNN 2017)
dc.relation.ispartofdatefrom2017-05-14
dc.relation.ispartofdateto2017-05-19
dc.relation.ispartoflocationAnchorage, Alaska, United States
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleSymbolic manipulation based on deep neural networks and its application to axiom discovery
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorSu, Kaile


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record