Re: SIGAda coverage in GCN

"Georg Bauhaus" <rm.tsoh.plus-bug.bauhaus@xxxxxxxxxxxxxxxxxx> wrote in
message news:4805107d$0$636$9b4e6d93@xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
roderick.chapman@xxxxxxxxxxxxxx wrote:
On Apr 14, 3:59 pm, roderick.chap...@xxxxxxxxxxxxxx wrote:
Today's new issue of GCN (athttp://www.gcn.com/print/27_8/46116-1.html)

Interestingly, this article as just been picked up by SlashDot...

As might have been expected, the article offers a lot to
foster the stereotypes about the expensive language that
drives weapons home; Ada is nothing more than Pascal, and
not a word about the fundamental type system and concurrency
until very late in the game. So the least interesting remarks
make it to the top. Oh well...

Oh well, indeed. Leemon Baird is a good guy, but they are talking about my
student Tyler Hallmark (at West Point, not the Air Force Academy) regarding
the Connect 4 report. Leemon's presentation - at least the one I saw - was
about implementing a neural net and getting the same performance as C.

:-S

Tyler did some nice work for an undergrad effort.

ABSTRACT
Parallel evolution of game evaluation functions in ada
This is an Ada experience report, where we conclude that Ada tasking and
distributed processing facilities make it a good research tool for
experimentation with algorithms that might eventually need multiple
processors. We implemented a genetic algorithm in Ada to create effective
computer players for Connect4. Key to our success was employing Ada tasking
and ALRM Annex E Distributed computing to harness a symmetric
multiproces-sor and a distributed machine with very few code changes. Easy
extension of an original single-task code to multi-tasking and distributed
variants-even though extension was not planned in advance-was essential to
timely completion. Using either the parallel or distributed implementation,
about 150 processor hours were sufficient to evolve players that neither the
GNU "Four-in-a-Row" Expert player nor the author could defeat. This
algorithm relies on human expertise to restrict the genetic search space.
Work is in progress on a new algorithm with near-zero encoded knowledge,
which will run on 220 distributed nodes within the same distributed
computing framework.


.

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