From: Sandra B Fan (sbfan_at_cs.washington.edu)
Date: Thu Oct 16 2003 - 20:13:35 PDT
"Evolving Robot Tank Controllers"
Jacob Eisenstein
Using a language he designed called TableRex, Eisenstein uses subsumption
architecture to genetically evolve Robocode tank controllers that beat
human-coded ones in simpler cases, but not in the most complex ones.
Two most important ideas: one is that genetic programming will not
necessarily evolve machines that act the way we want them to, so we must
be careful how we define their fitness function. For instance, at first,
the raw fitness function merely looked at the total number of points a
robot gains over a series of battles, but that led to robots that simply
won one or two big fights instead of trying to win them all. Also,
genetically evolving the robots led to robots that avoided firing, since
they could gain more points in other ways. Perhaps not firing really is
the best way to win, but we're placing our own biases on the robots by
trying to make them fire. So, we have to look carefully at what our goal
for the robots (or whatever we are trying to evolve) are, and whether or
not we want to place our own preconceptions on them.
The second most important idea is that it seems the main reason the more
complicated experiments could not be performed was due to lack of
resources-time and hardware-which means that this work is somewhat
promisingly in the right direction, given that we well eventually be able
to take care of those restraints.
One of the flaws is addressed in one of the important ideas. We are
assuming that a good robotic controller will fire, and so, we try to make
them fire. Maybe we shouldn't.
One possible open research question is that of co-evolution. Is there a
way to co-evolve programs without running into the problem that Eisenstein
did of robots that simply stand around doing nothing? The ability to
successfully co-evolve programs will make it easier to evolve programs in
which the features of a successful goal program are unclear, or to perform
a task for which other pre-made "robots" might not exist for them to test
against.
Another direction for research is finding out how to evolve robots for the
"multiple adversary, multiple starting positions" condition more quickly.
This is important because, of course, in real life, the conditions are not
as simple and restricted as the ones for which Eisenstein's tanks did
perform well. Eisenstein managed success at one level; now let's see if
we can take it to another.
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