Repairing numerical equations in analogically blended theories using reformation

Cheng-Hao Cai, Alan Bundy

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

The ABC system supports analogical abduction algorithms for knowledge transfer, e.g., existing logical rules are adapted, by reformation, into new rules by substituting symbols for similar ones. Although such knowledge transfer method can easily expand knowledge sets, it is likely to produce inconsistent knowledge, e.g., equations that do not fit target data in the real world. To solve this problem, we extend the classic reformation algorithm to repair numerical equations that violate target data. Equation reformation is achieved by weakening equation parameters when unblocking failed SLD-resolution proofs. The feasibility of numerical equation reformation is demonstrated by the automated repair of a faulty electrostatic force equation that is analogically transferred from the gravity equation.

Original languageEnglish
Title of host publicationProceedings of the 3rd Human-Like Computing Workshop (HLC 2022)
EditorsAlan Bundy, Denis Mareschal
PublisherCEUR-WS
Pages18-23
Number of pages6
Volume3227
Publication statusPublished - 2022
Externally publishedYes
EventInternational Workshop on Human-Like Computing Workshop 2022 - Windsor, United Kingdom
Duration: 28 Sept 202230 Sept 2022
Conference number: 3rd
https://ceur-ws.org/Vol-3227/ (Proceedings)
https://ijclr22.doc.ic.ac.uk/hlc2022.html/index.html#:~:text=The%203rd%20International%20Workshop%20on,%2C%2028%2D30%20September%202022. (Website)

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
ISSN (Print)1613-0073

Conference

ConferenceInternational Workshop on Human-Like Computing Workshop 2022
Abbreviated titleHLC 2022
Country/TerritoryUnited Kingdom
CityWindsor
Period28/09/2230/09/22
Internet address

Keywords

  • Analogical reasoning
  • Knowledge transfer
  • Reformation
  • Theory repair

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