David, Yes, this makes sense to me.
To go back to your original query, I still think you will find a rich community relevant to your work if you look into the MDL literature (which additionally does not rely on probability theory, though as I said I'd say it's equivalent). Perhaps this book might be helpful: http://www.amazon.com/Description-Principle-Adaptive-Computation-Learning/dp/0262072815/ref=sr_1_1?ie=UTF8&s=books&qid=1279036776&sr=8-1 It includes a (short-ish?) section comparing the pros/cons of MDL and Bayesianism, and examining some of the mathematical linkings between them-- with the aim of showing that MDL is a broader principle. I disagree there, of course. :) --Abram On Tue, Jul 13, 2010 at 10:01 AM, David Jones <[email protected]> wrote: > 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> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Abram Demski http://lo-tho.blogspot.com/ http://groups.google.com/group/one-logic ------------------------------------------- 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
