The (a, q) data modeling in probabilistic reasoning


  • Richard Douglas Kazan Federal University





This article considers a critical and experimental approach on the attributive and qualitative AI data modeling and data retrieval in computational probabilistic reasoning.

The mathematical correlation of X?Y in the d=dx/dy differentiations and its point based locations and matrix based predictions in Markov Models, Bayesian fields, and Rete’s algorithms, with the further development of non-linear ‘human-type’ reasoning in AI.

The new approach in the ternary system transition (decimal-binary) of Brusentsov-Bergman principle by its bound allocation in the ‘mirror-based’ system in tn-1… tn+1 powers, and hereon considers its further data retrieval for suitable matching and translation of probabilistic data differentiation.

The causation/probability matrix of this paper regards not only bound/free variable in x1, x2, x3, xn variables, but discovers and explains its further subsets in anXqn formula, where the supposition of d=X/Y regarded not as a mathematical placement of the variable X, but as its attributive (a) and qualitative (q) allocation in a certain value/relevance cell of the Probability Triangle of the ternary system. From where the automated differentiation retrieves only the most relevant/objective anXqn data cell, not the closest by the pre-set context, making the AI selections more assertive and preference based than linear.

Keywords: probability reasoning, artificial intelligence, computational logic, cognitive selection, AI computation.


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