Dear AGI, What is the maximal complexity of an environment in which prediction of future events in the environment is still computationally feasible?
In most 'realistic' environments prediction of the near future needs to be very precise and prediction of the far future can be vaguer, i.e. a large class of event types satisfies the prediction, in order for an agi agent to achieve its goals. (If prediction on the very long term also needs to be detailed and precise the environment is impossible for any agent, not?). In a formula, : (maximal vagueness of prediction that is allowed by agi agent, in order to plan and act successful to achieve goals) / (time scale of horizon of prediction) = some constant. Vagueness of prediction is the number of perception event types that satify the prediction. (count the perception event types at bit level). somewhat equivalent formula: (number of patterns of about time length l that occur in the environment) / (about l) = some constant A pattern here is a class of noisy variations on a pattern (10% noise, 20% noise? The smaller the constant gets the higher the noise ratio can be.). How large can those constants be? How complex may the environment be maximally for an ideal, but still realistic, agi agent (thus not a solomonof or AIXI agent) to be still succesful? Does somebody know how to calculate (and formalise) this? Bye, Arnoud ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
