On Nov 10, 2007 11:42 PM, Edward W. Porter <[EMAIL PROTECTED]> wrote: > > You say there is no magic in AIXI. Is it just make believe "Let X be the > best way to solve problems. Use X", or does it say something of value to > those like me who want to see real AGI's built? > Some observations that come to me from reading Marcus Hutter, you can see their worth:
(1) he gives rates of convergence for a posterior distribution to a real distribution, assuming that the real distribution has a non-zero prior probability (2) he shows that a weighted (=mean taking) predictor converges possibly exponentially faster than maximum likelihood predictor (3) he shows that the expectimax algorithm is optimal given unbound resources = you can view things "functionally": best possible policy (program, strategy), or iteratively (expectimax) (4) he discusses the issue of choosing horizon, reinforcement learning (=RL) work usually uses geometric discounting, Hutter shows that it gives (ln 0.5 / ln d) effective horizon (where d is the discount rate), (and it would be "theoretically" justified when the agent has probability d of surviving to next cycle) (5) he discusses RL from a general stance, e.g. classes of environments, application to learning frameworks more specific than RL (supervised learning, optimization) (but only theoretically) (6) he discusses the issues of dividing computation resources into using currently best strategy and searching for new strategy (in his "time-optimal algorithm for all well-specified problems") (7) his computational AIXItl model, assuming that it's the best out there since Marcus didn't come with something better :-), justifies some practical approaches of letting the competing policies estimate their expected utility: AIXItl only allows policies for which it can find a proof that the policy doesn't overestimate its utility, Eric Baum's market economy uses the policies' claims as a currency (cheating policies go bankrupt), accuracy-based Michigan-style learning classifier systems XCS use (evolutionary) selection pressure on the accuracy of the policies' claims (8) the "Kolmogorov-complexity-inspired distribution over programs" is related to new "better than genetic programming" approaches (Schmidhuber's OOPS, MOSES) (but perhaps only distantly) ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=63908199-d1781a
