Thanks Abram, I'll read up on it when I get a chance.
On Tue, Jul 13, 2010 at 12:03 PM, Abram Demski <[email protected]>wrote: > 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> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
