Jan 8, 2012

Brain reasoning, bayesian networks, abstraction and Markov chains

Surely just reinventing the wheel here, but human brain looks like a sophisticated bayesian Markov chain machine with abstraction capabilities.

It is a Markov chain machine in the sense that our brain, through learning and experience, builds and updates over time a large matrix of conditional probabilities. By counting the number of instances of concurrent events A, B, C, …, I, X in real life, the brain constantly updates the probability of outcome X given the occurrence of A, B, C, …, I. Or in mathematical terms, P(X|A,B,C,…I).

Abstraction capabilities allow then the brain to build upon fundamental conditional probabilities P1, P2, P3, Px based on direct experiences, to create a second, third and iteratively n-th layer of more complex probabilities, for example P(Px|P1,P2,P3,…).

This wealth of information could be encode in chromosome-like strings to be passed on and further processed.

Blog comments powered by Disqus