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PROFESSOR: One of the things
that you should probably have
00:00:24
noticed is as we're moving in
the terms, the problem sets are
00:00:29
getting less well defined. line
And I've seen a lot of email
00:00:36
traffic of the nature of what
should we do with this, what
00:00:40
should we do with that?
00:00:41
For example, suppose
the computer player
00:00:44
runs out of time.
00:00:45
Or the person runs out of
time playing the game.
00:00:49
Should it stop right away?
00:00:51
Should it just give
them zero score?
00:00:55
That's left out of
the problem set.
00:00:57
In part because one of the
things were trying to
00:01:00
accomplish is to have you folks
start noticing ambiguities
00:01:06
in problem statements.
00:01:07
Because that's life
in computing.
00:01:12
And so this is not like
a math problem set or
00:01:14
a physics problem set.
00:01:17
Or, like a high school physics
lab, where we all know what the
00:01:19
answer should be, and you could
fake your lab results anyway.
00:01:25
These are things where you're
going to have to kind
00:01:27
of figure it out.
00:01:29
And for most of these things,
all we ask is that you do
00:01:32
something reasonable, and
that you describe what
00:01:36
it is you're doing.
00:01:38
So I don't much care, for
example, whether you give the
00:01:41
human players zero points for
playing after the
00:01:46
Or you say you're done
when the time runs out.
00:01:50
Any of that -- thank you,
Sheila -- is ok with me.
00:01:57
What I don't want your
program to do is crash
00:02:00
when that happens.
00:02:02
Figure out something
reasonable and do it.
00:02:05
And again, we'll see this as an
increasing trend as we work
00:02:08
our way through the term.
00:02:11
The exception will be the
next problem set, which
00:02:14
will come out Friday.
00:02:17
Because that's not a
programming problem.
00:02:20
It's a problem, as you'll see,
designed to give you some
00:02:23
practice at dealing with some
of the, dare I say, more
00:02:28
theoretical concepts
we've covered in class.
00:02:31
Like algorithmic complexity.
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