> Novelty is recognized when a new PredicateNode (representing an observed > pattern) is created, and it's assessed that prior to the analysis of the > particular data the PredicateNode was just recognized in, the system would > have assigned that PredicateNode a much lower truth value. (That is: the > system has seen a pattern that it did not expect to see.)
So you're saying a newly formed PredicateNode normally has a low truth value, but PN's about novelty tend to have abnormally higher truth values? Or is it: novel Predicatenodes tend to have lower than normal truth values? > > Novelty is recognized when a "map" (a set of Atoms that share a coherent > activity pattern) is formed, which is dissimilar to any previously existing > maps. Are you familiar with the place cell system of the hippocampus as found in rats? I'll give you a brief synopsis in a new subject in case there's any ideas that you find useful. > > It should be noted that the rules for recognizing novelty are similar to the > rules for mentioning "learning". However, novelty focuses on the suddenness > of changes in truth value, whereas learning focuses on the total amount of > changes in truth value. The two are similar conceptually but different > quantitatively. Interesting idea, I'm still unclear about the specifics of how truth relates to novelty, but I get general idea. I'll wait for the nicer review article and leave you to your work. Thanks -Brad ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
