Thanks a lot for your comments, Abram... > It seems to me this would be better-described as Gibbs sampling for PLN > inference, rather than trying to compare it to MLN. :)
I adjusted the discussion a bit, based on this suggestion... though I did leave in some of the comparisons to MLN... > In steps 1-4, where does the sampling actually occur? As written, it looks > like it's just going to pass around probabilities using the ordinary PLN > rules. > > I think you meant: ... > Is this what you intended? Your suggestion is creative but not really what I meant... However, while I was swimming in the ocean this afternoon I realized that what I'd written before wasn't quite right... I have revised the blog post now, in a way that makes more sense I think -- now I make clear that the instantaneous truth value (formerly called the sampled truth value) is a second-order distribution, and one is sampling first-order distributions from it... > If the system gets probabilistic > knowledge input from outside, though, it's important that the sample values > be effected by this, too. yeah, that's true; I edited the post to note that... > Since PLN inference constitutes both learning and inference, it *might* be a > good idea to have far more information flow back to the samples from the > full probability estimates (in analogy with contrastive divergence, where > there is a tight intermingling of MCMC steps and weight updates). This might > mean occasionally re-sampling A from its current PLN truth value, rather > than using the procedure in steps 1-4. This is quite speculative. Sometimes that would be helpful, other times not, I guess... Hmmm ;) ... ben ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
