Abstraction in artificial intelligence and complex systems, by Lorenza Saitta, Jean-Daniel Zucker (auth.)

By Lorenza Saitta, Jean-Daniel Zucker (auth.)

Abstraction is a basic mechanism underlying either human and synthetic conception, illustration of data, reasoning and studying. This mechanism performs a very important position in lots of disciplines, particularly laptop Programming, common and synthetic imaginative and prescient, advanced platforms, man made Intelligence and desktop studying, artwork, and Cognitive Sciences. This booklet first offers the reader with an summary of the notions of abstraction proposed in a number of disciplines through evaluating either commonalities and changes. After discussing the characterizing homes of abstraction, a proper version, the KRA version, is gifted to catch them. This version makes the concept of abstraction simply acceptable through the advent of a suite of abstraction operators and abstraction styles, reusable throughout assorted domain names and functions. it's the effect of abstraction in man made Intelligence, advanced platforms and computing device studying which creates the middle of the publication. A basic framework, in line with the KRA version, is gifted, and its pragmatic strength is illustrated with 3 case reports: Model-based prognosis, Cartographic Generalization, and studying Hierarchical Hidden Markov Models.

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Abstract words denote ideas and concepts that cannot be experienced through our senses, such as “freedom” or “beauty”; on the contrary, concrete words denote objects that are part of the sensory reality, such as “chair” or “car”. As in the philosophical context, also in languages it is not always easy to classify a term as abstract or concrete. Moreover, this classification is dependent on the cultural background where the language originates. 2 Natural Language 19 (they viewed the world through the senses) [47].

An example is provided by the arithmetic operations, which, at the beginning, are learned as procedures, but then become objects whose properties (for instance, associativity) can be investigated. This transition is called encapsulation [142] or reification [482]. Ferrari argues that generalization, decontextualization and reification are all basic components of abstraction in Mathematics, but that abstraction cannot be identified with any single one of them. For instance, generalization, defined as an extensional inclusion relation, cannot exhaust the abstraction process, which also includes recognition of common properties, adoption of a compact axiom set, and definition of a notation systems to deal with newly defined concepts.

Finally, defining the granularity of a spatial region is a classical form of abstraction. According to Hobbs [252], granularity is a means to retrieve a simplified representation of a domain from more complex, richer representations. Spatial and temporal granularities are closely related to the concept of grain-size in a local spatial context, defined by Schmidtke [478]. Objects that are smaller than the grain-size can be disregarded as unimportant details. If objects smaller than the grain-size need to be accessed, a change of context is necessary: zooming out of a scene, a larger area is covered, but small details are lost, whereas zooming into a scene, smaller details are magnified, and objects further away become irrelevant.

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