> > Hi Ben, > > Thanks for the brain teaser! As a sometimes believer in Occam's Razor, I > think it makes sense to assume that Xi and Xj are indepenent, unless we know > otherwise. This simplifies things, and is the "rational" thing to do (for > some definition of rational ;->). So why not construct a bayes net modeling > the distributions, with causal links only where you _know_ two variables are > dependent? For reasoning about "orphan variables" (e.g., you know nothing > at all about Xi), just assume the average of all other distributions. If > you have P(Xi|Xj), and want P(Xj|Xi), fudge something together with Bayes' > rule. This isn't a complete solution, but its how I would start... Is this > one of the things you've tried? > > Cheers, > Moshe
As Pei Wang said: Intelligence is the ability to work and adapt to the environment with insufficient knowledge and resources. I think this is a core principle of AGI design and that a system that only makes inferences it *knows* are correct would be fairly uninteresting and incapable of performing in the real world. The fact that the information in the P(xi|xj) list is very incomplete is what makes the problem interesting. Or maybe I'm misinterpreting your intent. -Brad ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
