Open AccessExperimenting with Acquisition of and Matching to Systemic Classifiers
Author(s) -
S. V. Grigoryan,
Nairi P. Hakobyan
Publication year - 2018
Publication title -
mathematical problems of computer science
Language(s) - English
Resource type - Journals
eISSN - 2738-2788
pISSN - 2579-2784
DOI - 10.51408/1963-0026
Subject(s) - matching (statistics) , extension (predicate logic) , computer science , kernel (algebra) , frame (networking) , machine learning , artificial intelligence , class (philosophy) , knowledge acquisition , mathematics , programming language , discrete mathematics , telecommunications , statistics
We are modeling acquisition and classification abilities for the machine. The research line we follow, is based on the ideas of inventors of algorithms letting constructively model human computations and on some extension of those ideas aimed to model constructively other mental doings [1, 2]. We question the issues of acquisition of and matching to systemic classifiers and experimenting to prove the adequacy of our models. We experiment in the frame of RGT class of combinatorial problems for a RGT kernel problem, chess.