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.
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Jotsov, V., Petrova, P., Iliev, E. (2018). Learning Through Constraint Applications. In: Sgurev, V., Piuri, V., Jotsov, V. (eds) Learning Systems: From Theory to Practice. Studies in Computational Intelligence, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-75181-8_8
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