Right. The key issue is autogeny in the mental architecture. Learning will be unsupervised to start, with internal feedback from how well the system is expecting what it sees next. Then we move into a mode where imitation is the key, with the system trying to do what a person just did (e.g. catching the ball on the flipper, hitting some certain spot on the table, etc (note the flipper control is a full 1-dof signal, not just a 1-bit button). I can catch a ping-pong ball on a paddle in 3-d -- Tommy should be able to learn it in 2-d with an effective 0.1 G field!) To do this he'll have to develop concepts to describe what it is I'm trying to do.
There's a LOT you can do with 1 DOF output -- you could even imagine Tommy passing the Turing Test by sending Morse code with the flipper :-) Josh On Friday 11 May 2007 01:52:31 pm Derek Zahn wrote: > Bob Mottram writes:> In order to differentiate this from the rest of the robotics crowd you> need to avoid building a specialised pinball playing robot. > > I can't speak for JoSH, but I got the impression that playing "pinball" or anything similar was not the object, the object was to provide real sensor data in a somewhat limited domain to experiment with and observe concept formation. You'd like to see it develop object permanence, ball motion, gravity, "bouncing", and so on. The goal not being so much to impress people with performance on a vertical task but rather to use the task environment as a somewhat rich sandbox in which general purpose capabilities can be studied. ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
