Pei, Sorry for delayed reply. I answer point-by-point below.
On 10/11/07, Pei Wang <[EMAIL PROTECTED]> wrote: > > > > Basic rule for evidence-based > > estimation of implication in NARS seems to be roughly along the lines > > of term construction in my framework (I think there's much freedom in > > its choice, do you have other variants of it/justification for current > > choice relative to other possibilities which is not concerned with > > applicability to derivation of rules for abduction/induction/etc.?), > > There is some justification behind the design of every inference rule > (and its truth value function), not only abduction/induction. You can > find most in the book, and many are also in my other publications. I meant the basic rule of evidence measuring that considers extension and intension sets. There certainly is a justification for it, but there obviously are alternatives, so my question is about the choice of this extension/intension measuring above other options. > but I'm not sure about how you handle variations of structures (that > > is, how does system represents two structures which are similar in > > some sense and how it extracts the common part from them). It's > > difficult to see from basic rules if it's not addressed directly. > > The basic rules (deduction/abduction/induction/revision) ignore the > internal structure of compound terms. There are special inference > rules that handles the composition/decomposition of various compound > structure. Again, they are mostly given by the book. I didn't mean the structure of compound terms, but the structure of experience representation, which consists of a set of individual statements and terms that describe that experience. > For > > example, how will it see similarities and differences between > > 111222333 and 111122223333? Would it enable simple slippage between > > them? How will it learn these representations? > > Yes, the two can be recognized as similar, so the analogy rule can use > one as the other in certain situations. It'd be interesting to get an idea of how such things can be translated to internal representation that implements these operations. > Basic rule seems to require presence of terms at the same > > time, which for example can't be made neurologically plausible, unless > > semantics of terms is time-dependent (because neuron only knows that > > other neurons from which it received input fired some time in the > > past, and feature/term it represents if it chooses to fire is a > > statement about features represented by those other fired neurons in > > the past). > > It depends on what you mean by "presence of terms at the same time". > In NARS, all inference happens within a concept (because every > inference rule requires two premises sharing a term), so as far as two > beliefs are recalled at the same time, the basic rules can be applied. I mean the difference between experience of term in the present and experience of the same term (from I/O POV) that happened in the past. If these notions are represented by separate terms, how are they connected? I'm sorry if I'm asking about something that's being addressed in your book, I don't have a copy. > Why do you need so many rules? > > I didn't expect so many rules myself at the beginning. I add new rules > only when the existing ones are not enough for a situation. It will be > great if someone can find a simpler design. I feel that some of complexity comes from modeling of natural language statements. Do you agree? -- Vladimir Nesov mailto:[EMAIL PROTECTED] ----- 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=56112168-b226f2
