By Lorenza Saitta, Jean-Daniel Zucker (auth.)
Abstraction is a basic mechanism underlying either human and synthetic conception, illustration of information, reasoning and studying. This mechanism performs a very important function in lots of disciplines, significantly computing device Programming, normal and synthetic imaginative and prescient, advanced platforms, man made Intelligence and desktop studying, paintings, and Cognitive Sciences. This booklet first offers the reader with an outline of the notions of abstraction proposed in a variety of disciplines by means of evaluating either commonalities and transformations. After discussing the characterizing houses of abstraction, a proper version, the KRA version, is gifted to catch them. This version makes the inspiration of abstraction simply appropriate via the creation of a collection of abstraction operators and abstraction styles, reusable throughout diverse domain names and functions. it's the effect of abstraction in synthetic Intelligence, advanced structures and computer studying which creates the middle of the publication. A common framework, in keeping with the KRA version, is gifted, and its pragmatic energy is illustrated with 3 case experiences: Model-based prognosis, Cartographic Generalization, and studying Hierarchical Hidden Markov Models.
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Extra info for Abstraction in artificial intelligence and complex systems
In relational algebra, several among the operators can be interpreted, in an intuitive sense, as abstraction operators. For instance, given a relational table R with attributes (A1 , . . ,Air (R) hides R’s columns that are not mentioned in the operator. In an analogous way the selection operator σϕ (R) selects only those tuples for which the logical formula ϕ is true, hiding the remaining one. These operators clearly obey the principles of information hiding, because the omitted columns or rows in R are not deleted, but only hidden: they may be visualized again at any time.
An interesting discussion concerns the comparison between abstraction theories in classical Artificial Intelligence (where Barsalou sees them based on predicate calculus), and in connectionism. Barsalou identifies an abstraction as an attractor for a statistical combination of properties; here the abstraction is represented by the active units that characterize the attractor. The connectionist view of abstraction suffers from the problem of concept complexity, as neural nets have difficulties in representing structured scenarios.
An interesting aspect of abstraction is that an increase in the level of abstraction is paralleled by a deepening in the understanding of the connections among mathematical concepts. For instance, by abstracting the NonEuclidean geometry to “the study of properties invariant under a given group of symmetries” has revealed deep connections between geometry and abstract algebra. Moreover, abstraction can suggest direction of knowledge transfer among different domains. ” Thus, abstraction is about ignoring irrelevant features of an entity.
Abstraction in artificial intelligence and complex systems by Lorenza Saitta, Jean-Daniel Zucker (auth.)