> Perhaps someone can clarify some issues for me. > > I'm not good at math -- I can't follow the AIXI materials and I don't > know what Solomonoff induction is. So it's unclear to me how a > certain goal is mathematically defined in this uncertain, fuzzy > universe. > > What I'm assuming, at this point, is that AIXI and Solomonoff > induction depend on operation in a "somehow predictable" universe -- a > universe with some degree of entropy, so that its data is to some > extent "compressible". Is that more or less correct? > > And in that case, "goals" can be defined by feedback given to the > system, because the desired behaviour patterns it induces from the > feedback *predictably* lead to the desired outcomes, more or less? > > I'd appreciate if someone could tell me if I'm right or wrong on this, > or point me to some plain english resources on these issues, should > they exist. Thanks. > > -- > Cliff
The theorems about the AIXItl system tell you about how the system learns to behave according to a computable reward function. They say that the AIXItl system can learn to maximize reward as well as any other system, if it's given a certain (large) amount more resources than that system. If the universe is totally random, then NO AI system can display significant intelligence in it. In a random universe, the theorems just tell you that AIXItl doesn't do any worse than other AI systems -- because they all suck. But if a universe displays probabilistic rather than deterministic patterns, then AIXItl (and other AI systems such as Novamente) can do quite well. -- Ben ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
