On Sun, Sep 21, 2008 at 4:15 PM, William Pearson <[EMAIL PROTECTED]> wrote: > 2008/9/21 Pei Wang <[EMAIL PROTECTED]>: >> There are several issues involved in this example, though the basic is: >> >> (1) There is a decision to be made before a deadline (after 10 days), >> let's call it goal A, written as A? >> (2) At the current moment, the available information is not enough to >> support a confident conclusion, that is, the system has belief A<f,c>, >> though the confidence c is below the threshold (to trigger an >> immediate betting action). > > Can you get it so that without the knowledge of the possibility of > future information it would still act? E.g. is the threshold > adjustable in some way.
In the current implementation the threshold is just a constant (DECISION_THRESHOLD in http://code.google.com/p/open-nars/source/browse/trunk/nars/main/Parameters.java), but in the future the decision will also depend on other factors, such as the urgency of the goal --- if the decision must be taken very soon, the system won't wait for more information, but just use whatever evidence available. >> (3) It is known that future evidence B (the weather in russia 5 day >> before the deadline) will provide a better solution, that is, B==>A >> with a high <f,c>. > > It is this step I am interested in. > > Normally knowing X does not imply that you know X is useful for > solving all problems that X is useful for helping to solve. If I tell > you that Gordon Brown the current Prime Minister of the UK is in > political difficulties, you don't know that this will be useful for > answering a prize quiz question such as, "Who resigned after losing a > party leadership election this week" (it hasn't happened yet, but > might do). You might figure out that the fact I gave is useful for > this question and then guess the answer is Gordon Brown, without > having better information. Of course. > So there would need to be some form of search or linkage, so that you > construct the potential usefulness of facts as yet unknown, with their > usefulness as parts of the solution of problems. > > Does NARS do this? In NARS, what information will be useful for what problem is a kind of knowledge the system learns from its experience. It is not obtained by search, but by inference --- a constructive process. Even so, it is always possible that fact F will be helpful for solving problem P, though the system hasn't realized that yet. The system cannot afford the resources to exhaust all linkages, though will try to find as many as it can. > I should probably reformulate the scenario as the problem of which > question to phone a friend on "Who wants to be a millionaire". I shall > try and do so. Yes, it makes sense. Pei > Will Pearson > > > ------------------------------------------- > 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=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
