For visual perception, there are many reasons to think that a hierarchical architecture can be effective... this is one of the things you may find in dealing with real visual data but not with these toy examples...
E.g. in a spatiotemporal predictive hierarchy, the idea would be to create a predictive module (using an Occam heuristic, as you suggest) corresponding to each of a host of observed spatiotemporal regions, with modules corresponding to larger regions occurring higher up in the hierarchy... ben On Sun, Jun 27, 2010 at 10:09 AM, David Jones <davidher...@gmail.com> wrote: > Thanks Ben, > > Right, explanatory reasoning not new at all (also called abduction and > inference to the best explanation). But, what seems to be elusive is a > precise and algorithm method for implementing explanatory reasoning and > solving real problems, such as sensory perception. This is what I'm hoping > to solve. The theory has been there a while... How to effectively implement > it in a general way though, as far as I can tell, has never been solved. > > Dave > > On Sun, Jun 27, 2010 at 9:35 AM, Ben Goertzel <b...@goertzel.org> wrote: > >> >> Hi, >> >> I certainly agree with this method, but of course it's not original at >> all, it's pretty much the basis of algorithmic learning theory, right? >> >> Hutter's AIXI for instance works [very roughly speaking] by choosing the >> most compact program that, based on historical data, would have yielded >> maximum reward >> >> So yeah, this is the right idea... and your simple examples of it are >> nice... >> >> Eric Baum's whole book "What Is thought" is sort of an explanation of this >> idea in a human biology and psychology and AI context ;) >> >> ben >> >> On Sun, Jun 27, 2010 at 1:31 AM, David Jones <davidher...@gmail.com>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> >>> >> >> >> >> -- >> Ben Goertzel, PhD >> CEO, Novamente LLC and Biomind LLC >> CTO, Genescient Corp >> Vice Chairman, Humanity+ >> Advisor, Singularity University and Singularity Institute >> External Research Professor, Xiamen University, China >> b...@goertzel.org >> >> " >> “When nothing seems to help, I go look at a stonecutter hammering away at >> his rock, perhaps a hundred times without as much as a crack showing in it. >> Yet at the hundred and first blow it will split in two, and I know it was >> not that blow that did it, but all that had gone before.” >> >> *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> > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC CTO, Genescient Corp Vice Chairman, Humanity+ Advisor, Singularity University and Singularity Institute External Research Professor, Xiamen University, China b...@goertzel.org " “When nothing seems to help, I go look at a stonecutter hammering away at his rock, perhaps a hundred times without as much as a crack showing in it. Yet at the hundred and first blow it will split in two, and I know it was not that blow that did it, but all that had gone before.” ------------------------------------------- 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
