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Reasoning with Data - A New Challenge for AI?

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Scalable Uncertainty Management (SUM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9858))

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Abstract

Artificial intelligence (AI) traditionally deals with knowledge rather than with data (with the noticeable exception of machine learning). The term “knowledge” refers here to information with a generic flavor, while “data” refers to information pertaining to (collections of) particular cases. The formalization of reasoning patterns with data has been much less studied until now than knowledge representation and its application to knowledge-based systems and reasoning, possibly in presence of imperfect information. Data are positive in nature by manifesting the possibility of what is observed or reported, and contrast with knowledge that delimit the extent of what is potentially possible by specifying what is impossible. Reasoning from knowledge and data goes much beyond the application of knowledge to data as in expert systems. Besides, the idea of similarity naturally applies to data and gives birth to specific forms of reasoning such as case-based reasoning, case-based decision, or even case-based argumentation, interpolation, extrapolation, and analogical reasoning. Moreover, the analysis, the interpretation of data sets raise original reasoning problems for making sense of data. This article is a manifesto in favor of the study of types of reasoning which have been somewhat neglected in AI, by showing that AI should contribute to (knowledge) and data sciences, not only in the machine learning and in the data mining areas.

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Notes

  1. 1.

    A preliminary version of this paper exists in French [41].

  2. 2.

    Such a view is sometime termed as being “intensional”; see Pearl [38] who opposes it, in the case of rules, to “extensional” approaches where a (decision) rule would then express the license (rather than the obligation) to do something.

  3. 3.

    Strictly speaking, such a rule was usually modeled as meaning “if x is in A, then y can be chosen in B”, implicitly taking the view that it was reflecting commands already observed as being successful, and thus echoing positive information, or “extensional” rules [38]; see footnote 2.

  4. 4.

    An item is all the more a solution as it resembles to some example(s) in all important aspects, and is dissimilar from all counter-examples in some important aspect(s).

  5. 5.

    The use of these words here just refers to the application of a negation, and should not be confused with their use in other parts of the paper.

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Authors and Affiliations

  1. IRIT, Université Paul Sabatier, 118 route de Narbonne, 31062, Toulouse Cedex 09, France

    Henri Prade

  2. QCIS, University of Technology, Sydney, Australia

    Henri Prade

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  1. Henri Prade
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Correspondence to Henri Prade .

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  1. Cardiff University , Cardiff, United Kingdom

    Steven Schockaert

  2. Télécom ParisTech , Paris, Paris, France

    Pierre Senellart

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Prade, H. (2016). Reasoning with Data - A New Challenge for AI?. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_19

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