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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In Araújo (2014), we do an analysis of informational complexity similar to the one presented here about coherency, but these two concepts are different. In further works, we will examine the relation between them.
References
Alchourrón, C., Gärdenfors, P., & Makinson, D. (1985). On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic, 50, 510–530.
Araújo, A. (2014, forthcoming). A metrics for semantic informativity.
Aristotle. (1989). Prior analytics (R. Smith, Trans.). Indianapolis: Hackett Publishing Company.
Brandom, R. (1989). Making it explicit. Cambridge: Harvard University Press.
D’Agostino, M., & Floridi, L. (2009). The enduring scandal of deduction: Is propositional logic really uninformative? Synthese, 167(2), 271–315.
Ebbinghaus, H., & Flum, J. (1999). Finite model theory. Berlin: Springer.
Floridi, L. (2004). Outline of a theory of strongly semantic information. Minds and Machines, 14(2), 197–222.
Floridi, L. (2011). Philosophy of information. Oxford: Oxford University Press.
Hintikka, J. (1973). Logic, language games and information. Kantian themes in the philosophy of logic. Oxford: Clarendon Press.
Kroenke, D., & Auer, D. (2007). Database concepts. New York: Prentice Hall.
Sequoiah-Grayson, S. (2008). The scandal of deduction. Journal of Philosophical Logic, 37(1), 67–94.
Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134–1142.
Valiant, L. (2008). Knowledge infusion: In R. Hariharan & M. Mukund (Eds.), Pursuit of robustness in artificial intelligence. In FSTTCS 2008, Bangalore (pp. 415–422).
Wilson, D., & Sperber, D. (2004). Relevance theory. In L. Horn & G. Ward (Eds.), The handbook of pragmatics (pp. 607–632). Malden: Blackwell.
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].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
de Araújo, A.B. (2016). Semantic Information and Artificial Intelligence. In: Müller, V.C. (eds) Fundamental Issues of Artificial Intelligence. Synthese Library, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-26485-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-26485-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26483-7
Online ISBN: 978-3-319-26485-1
eBook Packages: Religion and PhilosophyPhilosophy and Religion (R0)