The (a, q) data modeling in probabilistic reasoning

20141020 https://doi.org/10.14419/jacst.v3i2.3270 
Abstract
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 nonlinear ‘humantype’ reasoning in AI.
The new approach in the ternary system transition (decimalbinary) of BrusentsovBergman principle by its bound allocation in the ‘mirrorbased’ system in tn1… 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 preset 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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How to Cite
Douglas, R. (2014). The (a, q) data modeling in probabilistic reasoning. Journal of Advanced Computer Science & Technology (JACST), 3(2), 179201. https://doi.org/10.14419/jacst.v3i2.3270Received date: 20140724
Accepted date: 20140830
Published date: 20141020