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From: loren@ingrid.llnl.gov (Loren Petrich)
Newsgroups: comp.ai
Subject: Re: Shooting pigeons
Message-ID: <93741@lll-winken.LLNL.GOV>
Date: 22 Mar 91 23:43:56 GMT
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In article <4754@syma.sussex.ac.uk> peterhi@syma.sussex.ac.uk (Peter Hickman) writes:
:I'm looking for some references to help me with my second year ( sorry I don't
:know what that's called in American ) Artificial Intelligence project. In a
:moment of pure insanity I submitted my project as to "Design and build a
:system that that shoot clay pigeon" the basis of the design is that everything
:can be done with a single camera and no more information than a shooter would
:have. There are three broad areas that I have to look at.

	You might want to check out alternatives to traditional AI,
such as Neural Nets, Fuzzy Logic, and the kind of robotics that Brooks
has used in his mechanical insects. I make these plugs because these
are techniques that have produced _results_, and that's what one is
supposed to get, right?

:    1)  Looking at the whole sky I need to detect significant notion ( other
:        than that of the clouds or trees etc ) where in therory a clay pigeon
:        is flying.

	Interesting question in Artificial Vision.

	To look for a Clay Pigeon, one need only look for something
that moves at a different speed from the background. This is easy to
detect for a static system, but that cannot be taken for granted. One
needs to compare successive images, and find out what has changed
abnormally.

	Frogs are known to have "bug detector" neurons in their brains
which fire whenever a small object moves past. [if I remember
correctly] A computerized "bug detector" might be a valuable
innovation in artificial vision.

	Detecting the motion of small objects may be a similar challenge.

:    2)  Locate the clay in the area of activity and find it's centre.

	Part of above.

:    3)  Calculate its trajectory and predict where it will be.

	One might want some strategy that learns from experience; a
Neural Net strategy, let us say. One minimizes the error function of
miss distance; one's inputs are the Clay Pigeon's position and
velocity and the positioning of one's gun.

	One can train by shooting several times and noticing the miss
distances. One trains to minimize the function (CP position, CP
velocity) -> (miss distance). Zero miss distance is, of course, what
is desired.

:    4)  Blast the ****** out of the sky.

	Easy if one has succeeded in training for (3).

:Point 3 seems to me to be the easiest and point 4 could well be academic as I
:have only 15 weeks as of Monday but what I should do for points 1 and 2 is
:much more open and I would greatly appreciate any references that you may feel
:that could be of help to me. The books I have gone through here on Computer
:Vision and Image Processing do not seem to cover the ground that I require in
:that they all assume static images of toy worlds. I need a mechanics outlook
:on the problems not a theorists at this stage.

	One may want to check out some of the papers in the IJCNN
Conferences on Neural Networks(?).

	Sorry, I don't know of any elementary NN textbooks.


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Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov

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