Abram, Thanks for the clarification Abram. I don't have a single way to deal with uncertainty. I try not to decide on a method ahead of time because what I really want to do is analyze the problems and find a solution. But, at the same time. I have looked at the probabilistic approaches and they don't seem to be sufficient to solve the problem as they are now. So, my inclination is to use methods that don't gloss over important details. For me, the most important way of dealing with uncertainty is through explanatory-type reasoning. But, explanatory reasoning has not been well defined yet. So, the implementation is not yet clear. That's where I am now.
I've begun to approach problems as follows. I try to break the problem down and answer the following questions: 1) How do we come up with or construct possible hypotheses. 2) How do we compare hypotheses to determine which is better. 3) How do we lower the uncertainty of hypotheses. 4) How do we determine the likelihood or strength of a single hypothesis all by itself. Is it sufficient on its own? With those questions in mind, the solution seems to be to break possible hypotheses down into pieces that are generally applicable. For example, in image analysis, a particular type of hypothesis might be related to 1) motion or 2) attachment relationships or 3) change or movement behavior of an object, etc. By breaking the possible hypotheses into very general pieces, you can apply them to just about any problem. With that as a tool, you can then develop general methods for resolving uncertainty of such hypotheses using explanatory scoring, consistency, and even statistical analysis. Does that make sense to you? Dave On Tue, Jul 13, 2010 at 2:29 AM, Abram Demski <[email protected]> wrote: > PS-- I am not denying that statistics is applied probability theory. :) > When I say they are different, what I mean is that saying "I'm going to use > probability theory" and "I'm going to use statistics" tend to indicate very > different approaches. Probability is a set of axioms, whereas statistics is > a set of methods. The probability theory camp tends to be bayesian, whereas > the stats camp tends to be frequentist. > > Your complaint that probability theory doesn't try to figure out why it was > wrong in the 30% (or whatever) it misses is a common objection. Probability > theory glosses over important detail, it encourages lazy thinking, etc. > However, this all depends on the space of hypotheses being examined. > Statistical methods will be prone to this objection because they are > essentially narrow-AI methods: they don't *try* to search in the space of > all hypotheses a human might consider. An AGI setup can and should have such > a large hypothesis space. Note that AIXI is typically formulated as using a > space of crisp (non-probabilistic) hypotheses, though probability theory is > used to reason about them. This means no theory it considers will gloss over > detail in this way: every theory completely explains the data. (I use AIXI > as a convenient example, not because I agree with it.) > > --Abram > ------------------------------------------- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
