[Edu-sig] FYI: PataPata postmortem link

Paul D. Fernhout pdfernhout at kurtz-fernhout.com
Sun Nov 26 22:50:02 CET 2006

Just as an FYI, as a way to wind up the PataPata project (or at least one 
phase of it), I wrote a lengthy postmortem critique of the PataPata 
project to date, plus ideas for where to go from here. You can read the 
critique by following this link:

"PataPata critique: the good, the bad, the ugly"

Comments welcome.

For reference, the PataPata project is/was """an experiment to support 
educational constructivism on the Python platform, inspired by "Squeak" 
and "Self", but going beyond those in a Pythonic way."""

 From the introduction: """It's been about three months from my last post 
to the PataPata list as well as my last major change to the system. I have 
been thinking about the system in the intervening time, and feel ready to 
produce a critique of it as an experiment (sort of as a, sadly, 
"postmortem" report). Others are welcome to chime in. This critique covers 
various good, bad, and ugly results from this experiment, and then 
outlines some thoughts on where to go next. This note
marks the end of this phase of the PataPata experiment. I am uncertain if 
this project on SourceForge will see more development, but I am certain if
there is more development on this particular SourceForge project, it will 
likely be in a radically different direction than the work published here 
to date. """


By the way, my decision to write a critique of PataPata was inspired in 
part by this paper by Drew McDermott, "Artificial Intelligence meets 
Natural Stupidity".

   http://portal.acm.org/citation.cfm?id=1045340 [fee based link]

The core of the paper is here:


 From there:

"McDermott explains how all research should be based on actual 
implementations, and be a thorough report on them. What is needed is a 
very clear picture of what was tried, what worked, what didn't, why didn't 
that work. And there must be a working program that later researchers can 
play with. Later research can build on these partial solutions, and report 
the exact improvements made since the previous version, the improvement in 
performance, etc. As McDermott states:

     The standard for such research should be a partial success, but AI as 
a field is starving for a few carefully documented failures. Anyone can 
think of several theses that could be improved stylistically and 
substantively by being rephrased as reports on failures. I can learn more 
by just being told why a technique won't work than by being made to read 
between the lines."

Thanks again to people here for your previous feedback on the project.

--Paul Fernhout

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