Abstract
In this chapter the ideas introduced in the previous chapters are formalized in the \(\mathcal{KRA }\) model, which takes into account a problem to be solved and the observations and theory necessary to solve it. Both observations and theory can be expressed at several levels of detail. The model describes how this change of resolution can be achieved. The model is not intended to cover all uses of abstraction, but it is rather targeted toward problems where observations are an important part of the solution process.
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- 1.
As already mentioned, the term “sensor” has to be intended in a wide sense, not only as a physical mechanism or apparatus. Acquiring information consists in applying a procedure that supplies the basic elements of the system under consideration.
- 2.
Notice that the names of objects are unique, so that they are the key to themselves.
- 3.
We may notice that the perception of the world does not provide names to the percepts, but it limits itself to register the outcomes of a set of sensors \(\varSigma \), grouping together those that come from the same sensors, and classifying them accordingly. This is an important point, because it allows the information about a system be decoupled from its linguistic denotation; for instance, when we see an object on top of another, we capture their relative spatial position, and this relation is not affected by the name (ontop, under, supporting, \(\ldots \)) that we give to the relation itself. Or, if we see some objects all the same color (say, red) we can perceptually group those objects, without knowing the name (“red”) of the color, nor even that the observed property has name “color”. The names are provided from outside the system.
- 4.
In Appendix C an overview of the relational algebra operators is provided.
- 5.
See Appendix D for a brief overview.
- 6.
See Van Dalen [545].
- 7.
The probabilistic setting is a possible extension of the \(\mathcal{{KRA}}\) model, very briefly mentioned in Chap. 10.
- 8.
See Sect. 2.1.
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© 2013 Springer Science+Business Media New York
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Saitta, L., Zucker, JD. (2013). The \(\mathcal{KRA }\) Model. In: Abstraction in Artificial Intelligence and Complex Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7052-6_6
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DOI: https://doi.org/10.1007/978-1-4614-7052-6_6
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