Probabilistic dynamic logic of cognition

Abstract

We developed an original approach to cognition, based on the previously developed theory of neural modelingfields and dynamic logic. This approach is based on the detailed analysis and solution of the problems of artificial intelligence ‐ combinatorial complexity and logic and probability synthesis. In this paper we interpret the theory of neural modeling fields and dynamic logic in terms of logic and probability, and obtain a Probabilistic Dynamic Logic of Cognition (PDLC). We interpret the PDLC at the neural level. As application we considered the task of the expert decision-making model approximation for the breast cancer diagnosis. First we extracted this model from the expert, using original procedure,basedonmonotoneBooleanfunctions.ThenweappliedPDLCforlearningthismodelfrom data.Becauseofthismodelmaybeinterpretedattheneurallevel,itmaybeconsideredasaresultof the expert brain learning. In the last section we demonstrate, that the model extracted from the expert and the model obtained by the expert learning are in good correspondence. This demonstrate that PDLC may be considered as a model of learning cognitive process.