A Network Representation of First-Order Logic That Uses Token Evolution for Inference.
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| Title: | A Network Representation of First-Order Logic That Uses Token Evolution for Inference. |
|---|---|
| Authors: | HIDEAKI SUZUKI1 HSUZUKI@nict.go.jp, MIKIO YOSHIDA2 yos@bbr.jp, HIDEFUMI SAWAI1 SAWAI@nict.go.jp |
| Source: | Journal of Information Science & Engineering. May2014, Vol. 30 Issue 3, p669-689. 18p. |
| Subjects: | Horn clauses, Logic programming, Logic circuits, Computer networks, Numerical analysis, Stochastic convergence |
| Abstract: | A method to represent first-order predicate logic (Horn clause logic) by a data-flow network is presented. Like a data-flow computer for a von Neumann program, the proposed network explicitly represents the logical structure of a declarative program by unlabeled edges and operation nodes. In the deduction, the network first propagates symbolic tokens to create an expanded AND/OR network by the backward deduction, and then executes unification by a newly developed method to solve simultaneous equations buried in the network. The paper argues the soundness and completeness of the network in a conventional way, then explains how a kind of ambiguous solution is obtained by the newly developed method. Numerical experiments are also conducted with some data-flow networks, and the method's convergence ability and scaling property to larger problems are investigated. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 97436997 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Network Representation of First-Order Logic That Uses Token Evolution for Inference. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22HIDEAKI+SUZUKI%22">HIDEAKI SUZUKI</searchLink><relatesTo>1</relatesTo><i> HSUZUKI@nict.go.jp</i><br /><searchLink fieldCode="AR" term="%22MIKIO+YOSHIDA%22">MIKIO YOSHIDA</searchLink><relatesTo>2</relatesTo><i> yos@bbr.jp</i><br /><searchLink fieldCode="AR" term="%22HIDEFUMI+SAWAI%22">HIDEFUMI SAWAI</searchLink><relatesTo>1</relatesTo><i> SAWAI@nict.go.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Information+Science+%26+Engineering%22">Journal of Information Science & Engineering</searchLink>. May2014, Vol. 30 Issue 3, p669-689. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Horn+clauses%22">Horn clauses</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+programming%22">Logic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+circuits%22">Logic circuits</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks%22">Computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+convergence%22">Stochastic convergence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A method to represent first-order predicate logic (Horn clause logic) by a data-flow network is presented. Like a data-flow computer for a von Neumann program, the proposed network explicitly represents the logical structure of a declarative program by unlabeled edges and operation nodes. In the deduction, the network first propagates symbolic tokens to create an expanded AND/OR network by the backward deduction, and then executes unification by a newly developed method to solve simultaneous equations buried in the network. The paper argues the soundness and completeness of the network in a conventional way, then explains how a kind of ambiguous solution is obtained by the newly developed method. Numerical experiments are also conducted with some data-flow networks, and the method's convergence ability and scaling property to larger problems are investigated. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 669 Subjects: – SubjectFull: Horn clauses Type: general – SubjectFull: Logic programming Type: general – SubjectFull: Logic circuits Type: general – SubjectFull: Computer networks Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Stochastic convergence Type: general Titles: – TitleFull: A Network Representation of First-Order Logic That Uses Token Evolution for Inference. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: HIDEAKI SUZUKI – PersonEntity: Name: NameFull: MIKIO YOSHIDA – PersonEntity: Name: NameFull: HIDEFUMI SAWAI IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 10162364 Numbering: – Type: volume Value: 30 – Type: issue Value: 3 Titles: – TitleFull: Journal of Information Science & Engineering Type: main |
| ResultId | 1 |
