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 <davidher...@gmail.com> 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 <jimbro...@gmail.com> wrote: > >> >> >> On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer <jimbro...@gmail.com> 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 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
