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Last modified: 2015.VII.19.14:08 - MIAÚ-RSS
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A universal artificial intelligence engine based on an anti-discriminative modelling principle

Leading article: 2018. September (MIAU No. 241.)
(Previous article: MIAU No. 240.)

Keywords: similarity analysis, consolidation of models

If the problems in the traffic optimization should be solved through artificial intelligence solutions, then two expectations can be declared: it is necessary to be capable of evaluating each condition-variation of the traffic system in form of a benchmarking process (following the Bostrom-principles), and it is also needed to ensure the possibility of exploration of connection between system components in order to be able to handle in a causal way. These two challenges have been covered through two, seemingly autonomous engines: one for anti-discrimation (objective, automated) modelling and an other one for generation of production functions - both belonging to the modules of the similarity analyses. The universality of the modelling is with other words the generalization potential of the modelling – an increasing along this scala expects to reduce the amount of modules involved into the solution. In this case it means: the mathematical connection between the two modules should be explored. The paper tries to demonstrate based on an example having an entire reproducibility, that the logics of exploration and evaluation are quasi the same in frame of the similarity analysis – where the well-known LP-engines are not capable of arbitrary approximation -however they are able to produce alternative staircases ensuring risk and/or sensibility analyses. Therefore, it can be declared – only one engine is necessary/existent/enough covering flexible modelling requirements for traffic optimization. The simplicity/simplification correspondents to the principle of Einstein, where it was declared: the simplest is better, and also to the principle of Knuth: Science is what we understand well enough to explain to a computer. Art is everything else we do. More (DOC) *** More (PDF)

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