:) You say that as if bayesian explanatory reasoning is the only way. There is much debate over bayesian explanatory reasoning and non-bayesian. There are pros and cons to bayesian methods. Likewise, there is the problem with non-bayesian methods because few have figured out how to do it effectively. I'm still going to pursue a non-bayesian approach because I believe there is likely more merit to it and that the short-comings can be overcome.
Dave On Thu, Jul 15, 2010 at 10:54 AM, Matt Mahoney <[email protected]> wrote: > Hypotheses are scored using Bayes law. Let D be your observed data and H be > your hypothesis. Then p(H|D) = p(D|H)p(H)/p(D). Since p(D) is constant, you > can remove it and rank hypotheses by p(D|H)p(H). > > p(H) can be estimated using the minimum description length principle or > Solomonoff induction. Ideally, p(H) = 2^-|H| where |H| is the length (in > bits) of the description of the hypothesis. The value is language dependent, > so this method is not perfect. > > > -- Matt Mahoney, [email protected] > > > ------------------------------ > *From:* David Jones <[email protected]> > *To:* agi <[email protected]> > *Sent:* Thu, July 15, 2010 10:22:44 AM > *Subject:* Re: [agi] How do we Score Hypotheses? > > It is no wonder that I'm having a hard time finding documentation on > hypothesis scoring. Few can agree on how to do it and there is much debate > about it. > > I noticed though that a big reason for the problems is that explanatory > reasoning is being applied to many diverse problems. I think, like I > mentioned before, that people should not try to come up with a single > universal rule set for applying explanatory reasoning to every possible > problem. So, maybe that's where the hold up is. > > I've been testing my ideas out on complex examples. But now I'm going to go > back to simplified model testing (although not as simple as black squares :) > ) and work my way up again. > > Dave > > On Wed, Jul 14, 2010 at 12:59 PM, David Jones <[email protected]>wrote: > >> Actually, I just realized that there is a way to included inductive >> knowledge and experience into this algorithm. Inductive knowledge and >> experience about a specific object or object type can be exploited to know >> which hypotheses in the past were successful, and therefore which hypothesis >> is most likely. By choosing the most likely hypothesis first, we skip a lot >> of messy hypothesis comparison processing and analysis. If we choose the >> right hypothesis first, all we really have to do is verify that this >> hypothesis reveals in the data what we expect to be there. If we confirm >> what we expect, that is reason enough not to look for other hypotheses >> because the data is explained by what we originally believed to be likely. >> We only look for additional hypotheses when we find something unexplained. >> And even then, we don't look at the whole problem. We only look at what we >> have to to explain the unexplained data. In fact, we could even ignore the >> unexplained data if we believe, from experience, that it isn't pertinent. >> >> I discovered this because I'm analyzing how a series of hypotheses are >> navigated when analyzing images. It seems to me that it is done very >> similarly to way we do it. We sort of confirm what we expect and try to >> explain what we don't expect. We try out hypotheses in a sort of trial and >> error manor and see how each hypothesis affects what we find in the image. >> If we confirm things because of the hypothesis, we are likely to keep it. We >> keep going, navigating the tree of hypotheses, conflicts and unexpected >> observations until we find a good hypothesis. Something like that. I'm >> attempting to construct an algorithm for doing this as I analyze specific >> problems. >> >> Dave >> >> >> On Wed, Jul 14, 2010 at 10:22 AM, David Jones <[email protected]>wrote: >> >>> What do you mean by definitive events? >>> >>> I guess the first problem I see with my approach is that the movement of >>> the window is also a hypothesis. I need to analyze it in more detail and see >>> how the tree of hypotheses affects the hypotheses regarding the "e"s on the >>> windows. >>> >>> What I believe is that these problems can be broken down into types of >>> hypotheses, types of events and types of relationships. then those types >>> can be reasoned about in a general way. If possible, then you have a method >>> for reasoning about any object that is covered by the types of hypotheses, >>> events and relationships that you have defined. >>> >>> How to reason about specific objects should not be preprogrammed. But, I >>> think the solution to this part of AGI is to find general ways to reason >>> about a small set of concepts that can be combined to describe specific >>> objects and situations. >>> >>> There are other parts to AGI that I am not considering yet. I believe the >>> problem has to be broken down into separate pieces and understood before >>> putting it back together into a complete system. I have not covered >>> inductive learning for example, which would be an important part of AGI. I >>> have also not yet incorporated learned experience into the algorithm, which >>> is also important. >>> >>> The general AI problem is way too complicated to consider all at once. I >>> simply can't solve hypothesis generation, comparison and disambiguation >>> while at the same time solving induction and experience-based reasoning. It >>> becomes unwieldly. So, I'm starting where I can and I'll work my way up to >>> the full complexity of the problem. >>> >>> I don't really understand what you mean here: "The central unsolved >>> problem, in my view, is: How can hypotheses be conceptually integrated along >>> with the observable definitive events of the problem to form good >>> explanatory connections that can mesh well with other knowledge about the >>> problem that is considered to be reliable. The second problem is finding >>> efficient ways to represent this complexity of knowledge so that the program >>> can utilize it efficiently." >>> >>> You also might want to include concrete problems to analyze for your >>> central problem suggestions. That would help define the problem a bit better >>> for analysis. >>> >>> Dave >>> >>> >>> On Wed, Jul 14, 2010 at 8:30 AM, Jim Bromer <[email protected]> wrote: >>> >>>> >>>> >>>> On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer <[email protected]>wrote: >>>> Even if you refined your model until it was just right, you would have >>>> only caught up to everyone else with a solution to a narrow AI problem. >>>> >>>> >>>> I did not mean that you would just have a solution to a narrow AI >>>> problem, but that your solution, if put in the form of scoring of points on >>>> the basis of the observation *of definitive* events, would constitute a >>>> narrow AI method. The central unsolved problem, in my view, is: How can >>>> hypotheses be conceptually integrated along with the observable definitive >>>> events of the problem to form good explanatory connections that can mesh >>>> well with other knowledge about the problem that is considered to be >>>> reliable. The second problem is finding efficient ways to represent this >>>> complexity of knowledge so that the program can utilize it efficiently. >>>> >>>> *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> > <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> > <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
