David, That's why, imho, the rules need to be *learned* (and, when need be, unlearned). IE, what we need to work on is general learning algorithms, not general visual processing algorithms.
As you say, there's not even such a thing as a general visual processing algorithm. Learning algorithms suffer similar environment-dependence, but (by their nature) not as severe... --Abram On Thu, Jul 8, 2010 at 3:17 PM, David Jones <[email protected]> wrote: > I've learned something really interesting today. I realized that general > rules of inference probably don't really exists. There is no such thing as > complete generality for these problems. The rules of inference that work for > one environment would fail in alien environments. > > So, I have to modify my approach to solving these problems. As I studied > over simplified problems, I realized that there are probably an infinite > number of environments with their own behaviors that are not representative > of the environments we want to put a general AI in. > > So, it is not ok to just come up with any case study and solve it. The case > study has to actually be representative of a problem we want to solve in an > environment we want to apply AI. Otherwise the solution required will take > too long to develop because of it tries to accommodate too much > "generality". As I mentioned, such a general solution is likely impossible. > So, someone could easily get stuck trying to solve an impossible task of > creating one general solution to too many problems that don't allow a > general solution. > > The best course is a balance between the time required to write a very > general solution and the time required to write less general solutions for > multiple problem types and environments. The best way to do this is to > choose representative case studies to solve and make sure the solutions are > truth-tropic and justified for the environments they are to be applied. > > Dave > > > On Sun, Jun 27, 2010 at 1:31 AM, David Jones <[email protected]>wrote: > >> A method for comparing hypotheses in explanatory-based reasoning: * >> >> We prefer the hypothesis or explanation that ***expects* more >> observations. If both explanations expect the same observations, then the >> simpler of the two is preferred (because the unnecessary terms of the more >> complicated explanation do not add to the predictive power).* >> >> *Why are expected events so important?* They are a measure of 1) >> explanatory power and 2) predictive power. The more predictive and the more >> explanatory a hypothesis is, the more likely the hypothesis is when compared >> to a competing hypothesis. >> >> Here are two case studies I've been analyzing from sensory perception of >> simplified visual input: >> The goal of the case studies is to answer the following: How do you >> generate the most likely motion hypothesis in a way that is general and >> applicable to AGI? >> *Case Study 1)* Here is a link to an example: animated gif of two black >> squares move from left to >> right<http://practicalai.org/images/CaseStudy1.gif>. >> *Description: *Two black squares are moving in unison from left to right >> across a white screen. In each frame the black squares shift to the right so >> that square 1 steals square 2's original position and square two moves an >> equal distance to the right. >> *Case Study 2) *Here is a link to an example: the interrupted >> square<http://practicalai.org/images/CaseStudy2.gif>. >> *Description:* A single square is moving from left to right. Suddenly in >> the third frame, a single black square is added in the middle of the >> expected path of the original black square. This second square just stays >> there. So, what happened? Did the square moving from left to right keep >> moving? Or did it stop and then another square suddenly appeared and moved >> from left to right? >> >> *Here is a simplified version of how we solve case study 1: >> *The important hypotheses to consider are: >> 1) the square from frame 1 of the video that has a very close position to >> the square from frame 2 should be matched (we hypothesize that they are the >> same square and that any difference in position is motion). So, what >> happens is that in each two frames of the video, we only match one square. >> The other square goes unmatched. >> 2) We do the same thing as in hypothesis #1, but this time we also match >> the remaining squares and hypothesize motion as follows: the first square >> jumps over the second square from left to right. We hypothesize that this >> happens over and over in each frame of the video. Square 2 stops and square >> 1 jumps over it.... over and over again. >> 3) We hypothesize that both squares move to the right in unison. This is >> the correct hypothesis. >> >> So, why should we prefer the correct hypothesis, #3 over the other two? >> >> Well, first of all, #3 is correct because it has the most explanatory >> power of the three and is the simplest of the three. Simpler is better >> because, with the given evidence and information, there is no reason to >> desire a more complicated hypothesis such as #2. >> >> So, the answer to the question is because explanation #3 expects the most >> observations, such as: >> 1) the consistent relative positions of the squares in each frame are >> expected. >> 2) It also expects their new positions in each from based on velocity >> calculations. >> 3) It expects both squares to occur in each frame. >> >> Explanation 1 ignores 1 square from each frame of the video, because it >> can't match it. Hypothesis #1 doesn't have a reason for why the a new square >> appears in each frame and why one disappears. It doesn't expect these >> observations. In fact, explanation 1 doesn't expect anything that happens >> because something new happens in each frame, which doesn't give it a chance >> to confirm its hypotheses in subsequent frames. >> >> The power of this method is immediately clear. It is general and it solves >> the problem very cleanly. >> >> *Here is a simplified version of how we solve case study 2:* >> We expect the original square to move at a similar velocity from left to >> right because we hypothesized that it did move from left to right and we >> calculated its velocity. If this expectation is confirmed, then it is more >> likely than saying that the square suddenly stopped and another started >> moving. Such a change would be unexpected and such a conclusion would be >> unjustifiable. >> >> I also believe that explanations which generate fewer incorrect >> expectations should be preferred over those that more incorrect >> expectations. >> >> The idea I came up with earlier this month regarding high frame rates to >> reduce uncertainty is still applicable. It is important that all generated >> hypotheses have as low uncertainty as possible given our constraints and >> resources available. >> >> I thought I'd share my progress with you all. I'll be testing the ideas on >> test cases such as the ones I mentioned in the coming days and weeks. >> >> Dave >> > > *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
