Learning Through Constraint Applications

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 756)

Abstract

Solutions are considered for focusing agent attention aiming at lower computational complexity of information processing. New logical-based types of constraints and applications of Puzzle methods have been considered as a specific standard for improvement of different Data Science problems, especially via SAS Enterprise Miner tools. It is shown how the prognostic models have been improved using the considered original data-driven approaches or algorithms or by using different types of Binding, Pointing and different classical constraints. As a result, new types of non-implicative causal relations are revealed. Different modifications of proposed Puzzle methods have been researched aiming at better control of different types of constraints and elaboration of new, contemporary and more universal evolutionary applications of logical and statistical methods in one system.

Keywords

Constraint satisfaction Semantic conflict Data-driven applications Puzzle methods Data science Machine learning Agent Logical applications Statistics 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Library Studies and Information Technologies (ULSIT)SofiaBulgaria

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