Abstract
One of the field in which models of abstraction have been proposed is Artificial Intelligence (AI). This chapter has two parts: one presents an overview of the formal models, either syntactic or semantic, mostly based on logical approaches, whereas the other one describes the use of abstraction in AI, starting from ABSTRIP, and spanning planning, knowledge representation, Constraint Satisfaction Problems, and Multi-Agent Systems.
“Abstraction is the essence of intelligence and
the hard part of the problems being solved”
[Brooks, 1991]
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Notes
- 1.
A kind of bi-directional search [163].
- 2.
A constraint \(C\) is often called global when processing \(C\) as a whole gives better results than processing any conjunction of constraints that is semantically equivalent to \(C\) [56].
- 3.
For example abstraction in games [205, 486], or abstraction in networks [466, 472, 586], or abstraction in Multiple Representation Modeling (MRM) [13, 41, 123, 192], or many others.
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Saitta, L., Zucker, JD. (2013). Abstraction in Artificial Intelligence. In: Abstraction in Artificial Intelligence and Complex Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7052-6_3
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DOI: https://doi.org/10.1007/978-1-4614-7052-6_3
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