Discovering numeric laws, a case study: CO2 fugacity in the ocean

Kasun Wickramaratna, Miroslav Kubat, Peter J Minnett

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The idea to automate the discovery of numeric laws goes back to early 1980's when some authors showed how to use to this end AI search techniques. Later, the community somewhat lost interest in this task, reasoning that it was unlikely that a computer program would ever "outperform" human intuition supported by background knowledge. Only recently did some authors manage to overcome this scepticism: their programs were able to discover new numeric laws in soft-science domains such as ecology and psychology. In the case study reported here, we describe a successful attempt to assist ocean chemists in their attempts to predict absorption rate of CO2 in certain regions of the ocean. The system we have developed has found equations that outperform those suggested by field experts assisted by regression techniques. Our experience indicates that more attention should be paid to the following: the impact of spatio-temporal aspects, the existence of "hidden causes" (not directly reflected in a given set of variables), and the need for a field expert to post-process the results.

Original languageEnglish
Pages (from-to)379-391
Number of pages13
JournalIntelligent Data Analysis
Volume12
Issue number4
StatePublished - Sep 11 2008

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Ecology
Numerics
Ocean
Computer program listings
Absorption
Reasoning
Regression
Predict
Experience
Psychology
Human
Knowledge
Background
Community

Keywords

  • Genetic algorithm
  • Ocean chemistry
  • Quantitative law discovery

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition

Cite this

Discovering numeric laws, a case study : CO2 fugacity in the ocean. / Wickramaratna, Kasun; Kubat, Miroslav; Minnett, Peter J.

In: Intelligent Data Analysis, Vol. 12, No. 4, 11.09.2008, p. 379-391.

Research output: Contribution to journalArticle

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