Sorry I don't have the time to type a detailed reply, but for your second point, see the example in http://www.cogsci.indiana.edu/pub/wang.fuzziness.ps , page 9, 4th paragraph:
If these two types of uncertainty [randomness and fuzziness] are different, why bother to treat them in an uniform way? The basic reason is: in many practical problems, they are involved with each other. Smets stressed the importance of this issue, and provided some examples, in which randomness and fuzziness are encountered in the same sentence ([20]). It is also true for inferences. Let's take medical diagnosis as an example. When a doctor want to determine whether a patient A is suffering from disease D, (at least) two types of information need to be taken into account: (1) whether A has D's symptoms, and (2) whether D is a common illness. Here (1) is evaluated by comparing A's symptoms with D's typical symptoms, so the result is usually fuzzy, and (2) is determined by previous statistics. After the total certainty of "A is suffering from D" is evaluated, it should be combined with the certainty of "T is a proper treatment to D" (which is usually a statistic statement, too) to get the doctor's "degree of belief" for "T should be applied to A". In such a situation (which is the usual case, rather than an exception), even if randomness and fuzziness can be distinguished in the premises, they are mixed in the middle and final conclusions. Pei On Mon, Sep 8, 2008 at 3:55 PM, YKY (Yan King Yin) <[EMAIL PROTECTED]> wrote: > A somewhat revised version of my paper is at: > http://www.geocities.com/genericai/AGI-ch4-logic-9Sep2008.pdf > (sorry it is now a book chapter and the bookmarks are lost when extracting) > > On Tue, Sep 2, 2008 at 7:05 PM, Pei Wang <[EMAIL PROTECTED]> wrote: >>> >>> I intend to use NARS confidence in a way compatible with >>> probability... > >> I'm pretty sure it won't, as I argued in several publications, such as >> http://nars.wang.googlepages.com/wang.confidence.pdf and the book. > > I understood your argument about defining the confidence c, and agree > with it. But I don't see why c cannot be used together with f (as > *traditional* probability). > >> In summary, I don't think it is a good idea to mix B, P, and Z. As Ben >> said, the key is semantics, that is, what is measured by your truth >> values. I prefer a unified treatment than a hybrid, because the former >> is semantically consistent, while the later isn't. > > My logic actually does *not* mix B, P, and Z. They are kept > orthogonal, and so the semantics can be very simple. Your approach > mixes fuzziness with probability which can result in ambiguity in some > everyday examples: eg, John tries to find a 0.9 pretty girl (degree) > vs Mary is 0.9 likely to be pretty (probability). The difference is > real, but subtle, and I agree that you can mix them but you must > always acknowledge that the measure is mixed. > > Maybe you've mistaken what I'm trying to do, 'cause my theory should > not be semantically confusing... > > YKY > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
