The (a,q) data modeling in probabilistic reasoning (2014)
The first attempt to implement the 'doubt' and 'choice' categories into the AI inference method. Probability reasoning automation is demonstrated on simple Pascal-driven logic. The idea of AI receiving human-like traits is explained via the Bayesian type probability with abstract 'a' and 'q' data modeling implementations; where 'a' is an abstract category of AI data, juxtaposed to the probable, logical 'q' choice. This paper is the first approach to expand the classic P(A|B) theorem into more selective, choice-driven options in reckoning.
Imaginary number probability in Bayesian type inference (2015)
Imaginary number probability approaches the 'a' and 'q' data modeling in an attempt to implement mathematical principles of AI inference into the object-oriented programming languages (C, C++, etc), in order to categorize 'doubt' and 'choice' of the AI, thus to avoid the machine-like data processing and develop human-like traits. A mathematical attempt to realize the principles of 'choice', 'possible choice' and 'impossible choice', in order to expand the classic understanding of the Bayes theorem and inspire further research in this direction.