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[DPRG] Great Roborama!

Subject: [DPRG] Great Roborama!
From: Rud Merriam k5rud at arrl.net
Date: Wed Nov 26 10:45:49 CST 2008

Jose',
 
Thanks much for the reply. 
 
I have looked at and tried to implement a system called Push, see
http://hampshire.edu/lspector/push.html, which is similar to your approach.
A Push system consists of a number of stacks (integer, code, name, etc...)
that contain values. The code stack is executed by popping opcodes and then
performing the operation on the stacks, including the code stack. The result
of the executed code is used to determine fitness. It is then evolved by
mating with other successful codes. 
 
It sounds like your execution environment is simpler than the Push system
which is rather complex. 
 
I may get back to you on other ideas in the future.


Rud Merriam K5RUD
ARES AEC Montgomery County, TX 
http://TheHamNetwork.net <http://thehamnetwork.net/>  

-----Original Message-----
From: Francisco Jose Ayala [mailto:ze at neuroblast.net] 
Sent: Wednesday, November 26, 2008 9:24 AM
To: Rud Merriam
Cc: DPRG
Subject: Re: [DPRG] Great Roborama!





Hi Jose,
 
Can you share more on how you develop the neural nets, do the simulations,
and genetic algorithms? 


Be glad to.  I discussed what the genomes look like and a bit about how they
evolve in my previous email response to Jacob.  Here I'll add a few more
points.


The neural systems (I call them brains) that we develop have very little in
common with conventional artificial neural networks (ANNs).  ANNs consist of
layers of neurons that each perform identical threshold gating functions,
and the layers are fully interconnected, meaning that every neuron in a
given layer is connected to every other neuron in the layer ahead of it and
behind it.

By contrast, the neurons in our brains are free to evolve almost any kind of
mathematical function, each neuron in a given genome is usually quite
different from every other, and the connections between them are free to
evolve just about any kind of pattern.  You can see what one of our earliest
and simplest brains (which we evolved to balance an inverted pendulum) looks
like, along with a detailed description of how it works, here
<http://www.neuroblast.net/angler.shtml> .

A genome consists of genes (encoded as parse trees) for multiple neurons.  A
brain consists of multiple instantiations of that set of neurons.  A
population consists of multiple brains.  Every generation, every brain
within a population is evaluated on its performance on a given task (e.g.
line-following).  The brains that perform best stand the best chance of
contributing their genes to the gene pool of the next generation.



Is this software proprietary to you or available from others?



Our software and evolutionary system is proprietary.  We normally do not
make it available to others, but we would be willing to discuss sharing with
fellow DPRGers.  If you have any ideas for cool robotics (or other)
applications that could use this kind of AI, let's talk about it.



I have had an interest in Genetic Algorithms and Programming for awhile. 


My background is in molecular evolutionary genetics, so naturally this
heavily influenced my approach to AI.  The only design paradigm to ever
produce a genuinely intelligent system is biological evolution.  And the
only kind of intelligent system it every produced was neural (that is,
simple interactions among simple processing elements, from which potentially
emerges great computational complexity).  





Also have done some work with the NASA CLIPS expert system.


Very cool.



I gather you are not training the neural network per se, but using GA to
derive networks and then test in simulation?


Right.  There are no training sets, backpropagation, descent algorithms,
etc.  The brains either perform well or the selection regime will purge them
from the population.  To paraphrase Yoda: Do or do not; there is no train.


That is not to say, however, that we cannot evolve brains that learn.  In
fact, we are working on evolving brains that learn through operant
conditioning, so their behaviors can be taught, much like you might teach
tricks to a puppy or a dolphin.  


This opens up the possibility for some interesting robotics applications.
Again, if any DPRGers have any ideas, let's talk.


Cheers,
Jose'








Rud Merriam K5RUD
ARES AEC Montgomery County, TX 
http://TheHamNetwork.net <http://thehamnetwork.net/>  

-----Original Message-----
From: dprglist-bounces at dprg.org [mailto:dprglist-bounces at dprg.org] On Behalf
Of Francisco Jose Ayala
Sent: Monday, November 24, 2008 4:28 PM
To: dprglist at dprg.org
Subject: Re: [DPRG] Great Roborama!


Yes, our robots, 3piRoBrainia I and II, were driven by AI.   

Specifically, they were controlled by a neural system that we evolved in a
genetic algorithm-like virtual world of genes, genomes, organisms and
populations that all compete against one another in a rigorous selection
regime.  The end product is a "brain" composed of many identical subunits
(just as a human cortex is a massive array consisting of countless
repetitions of the same assemblage of neurons). 

The 3piRoBrainia brains consist of five such subunits, one for each
reflectance sensor.  You can see a picture of this brain here:
http://www.neuroblast.net/images/20081121pop17gen690hiBrn5.jpg

Each collection of blue circles is a parse tree that defines the processing
of a single neuron.  The lines represent neural connections, or axons: pink
lines are for connections within a subunit, and green lines for connections
between adjacent subunits.  Yellow circles represent the location of sensor
inputs.

We often have no idea how these brains actually work; we simply select for
certain capabilities, such as line following, and then load the resulting
brains onto the robot.  The brains get better with every generation.  The
brains we entered into the roborama competition were produced in just 48
hours of evolution.  We'll bring improved brains to an upcoming RBNO.

Also, Dale Wheat is creating a modification of the 3pi for us that will have
a 2D array of 3x8 sensors, which will give the brains a much better picture
of the road ahead.  We'll have brains for that soon.

Cheers,
Jose'



On Nov 24, 2008, at 8:34 AM, DeltaGraph at aol.com wrote:



Dale,
Maybe you can elaborate?
 
Was that Jose Ayala and Carl Sturner's 3pi robots?
I heard brief mention of a "Brain".
 
Their robots were right up there in the fast run times.
 
Ron




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