My opinion is that this is as good a place to start as any.  At least you
are dealing with an actual problem, your trying different stuff out, and you
seem like you are willing to actually try it out.
The problem is that the scoring is based on a superficial model of
conceptual integration, where, for some reason, you believe that the answer
to the essential problem includes a method of rephrasing the problem into
simpler questions which then can magically be answered.  You are worried
about the finery without first creating the structure.  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.
Jim Bromer

On Tue, Jul 13, 2010 at 8:15 PM, David Jones <davidher...@gmail.com> wrote:

> I've been trying to figure out how to score hypotheses. Do you guys have
> any constructive ideas about how to define the way you score hypotheses like
> these a little better? I'll define the problem below in detail. I know Abram
> mentioned MDL, which I'm about to look into. Does that even apply to this
> sort of thing?
>
> I came up with a hypothesis scoring idea. It goes as follows
>
> *Rule 1:* Hypotheses are compared only 1 at a time.
> *Rule 2:* If hypothesis 1 predicts/expects/anticipates something, then you
> add (+1) to its score and subtract (-1) from hypothesis 2 if it doesn't also
> anticipate the observation. (Note:When comparing only 2 hypotheses, it may
> actually not be necessary to subtract from the competing hypothesis I
> guess.)
>
> *Here is the specific problem I'm analyzing: *Let's say that you have two
> window objects that contain the same letter, such as the letter "e". In
> frame 0, the first window object is visible. In frame 1, window 1 moves a
> bit. In frame 2 though, the second window object appears and completely
> occludes the first window object. So, if you only look at the letter "e"
> from frame 0 to frame 2, it looks like it never disappears and it just
> moves. But that's not what happens. There are two independent instances of
> the letter "e". But, how do we get the algorithm to figure this out in a
> general way? How do we get it to compare the two possible hypotheses (1
> object or two objects) and decide that one is better than the other? That is
> what the hypothesis scoring method is for.
>
> *Algorithm Description and Details*
> *Hypothesis 1:* there are two separate objects... there are two separate
> instances of the letter "e"
> *Hypothesis 2:* there is only one letter object... only one letter "e"
> that occurs in all the frames of the video.
>
> *Time 0: object 1*
>
> *Time 1: "e" moves rigidly with object 1*
>     H1: +1 compared to h2 because we expect the e to move rigidly with the
> first object, rather than independently from the first object.
>     H2: -1 compared to h1 because we don't expect the first object to move
> rigidly with "e" but h1 does.
>
> *Time 2: object 2 appears and completely occludes object 1.  Object 1 and
> 2 both have the letter "e" on them. So, to a dumb algorithm, it looks as if
> the "e" moved between the two frames of the video.*
>     H1: -1 compared to h2 because we don't expect what h2 expects.
>     H2: +1 compared to h1 "e" moves independently of the first window
>
> *Time 3: "e" moves rigidly with object 2*
>     H1: +1 compared to h2 "e" moves with second object.
>     H2: -1 compared to h1
> *Time 4: "e" moves rigidly with object 2*
>     H1: +1 compared to h2 "e" moves with second object.
>     H2: -1 compared to h1
> *Time 5: "e" moves rigidly with object 2*
>     H1: +1 compared to h2 "e" moves with second object.
>     H2: -1 compared to h1
>
> *After 5 video frames the score is: *
> H1: +3
> H2: -3
>
> Dave
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