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Semantic Information and Artificial Intelligence

  • Anderson Beraldo de AraújoEmail author
Chapter
Part of the Synthese Library book series (SYLI, volume 376)

Abstract

For a computational system to be intelligent, it should be able to perform, at least, basic deductions. Nonetheless, since deductions are, in some sense, equivalent to tautologies, it seems that they do not provide new information. In order to analyze this problem, the present article proposes a measure of the degree of semantic informativity of valid deductions. Concepts of coherency and relevancy, displayed in terms of insertions and deletions on databases, are used to define semantic informativity. In this way, the article shows that a solution to the problem about informativity of deductions provides a heuristic principle to improve the deductive power of computational systems.

Keywords

Semantic information Artifial intelligence Scandal of deduction 

Notes

Acknowledgements

I would like to thank Viviane Beraldo de Araújo for her support, to Luciano Floridi for his comments on my talk given at PT-AI2013, and to Pedro Carrasqueira for his comments and to an anonymous referee for his (her) criticism on a previous version of this paper. This work was supported by São Paulo Research Foundation (FAPESP) [2011/07781-2].

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Center for Natural and Human Sciences (CCNH)Federal University of ABC (UFABC)São Bernardo do CampoBrazil

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