Lecture 23:
Programming with Pictures &
Programming Wet Stuff
“ToonTalk is a video game for making
video games.”
David Kahn, age 10.
“It's an animated world that lets kids
make, run, debug, and trade programs.”
Ken Kahn, David's dad
CS655: Programming Languages
David Evans
University of Virginia
http://www.cs.virginia.edu/~evans
Computer Science
Menu
• Rotunda Presentations
• Non-text based Programming
• Amorphous Computing
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Course Evaluations
• You should have received email
• Fill out the official evaluation at:
http://www.people.virginia.edu/~ad4n/seas_evals/
• Must fill in before May 5
• Once you do, send me email to get the real
evaluation
– Specific questions like the midterm evaluation
– Fill out after the final (this is an important part of
the course to evaluate, no matter what SEAS
thinks!)
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Rotunda Presentations
• Monday, 6:30 pm
• Laptop projector, screen,
laptop with PowerPoint
– If you need something
else, let me know
• Not all group members
need talk
• Aim for about 15 minutes
(no more than 20)
• Invite guests (but number
limited)
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Giving a Good Rotunda Talk
• Pretend its a research conference talk
– Your audience knows about the field, but
not your specific area
– Your goal is to make them want to learn
more about your work
• A good talk tells a story
– Not a collection of slides
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Beginning
• Motivate your work
– Convey why the problem you are solving is
interesting, important and exciting
• It helps when you think so yourself, but act if you don’t
– Place your work in context: how is it different from
what others have done
• Can do this at end instead
• Teaser for your results – why should we listen
to the rest of the talk?
– Don’t need a full outline, but let audience know
enough so they want to listen to the rest
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Middle
• Explain your solution
– Don’t be comprehensive – convey the big picture
– Use pictures, metaphors, examples, etc. to explain
what you did
• Convey one technical nugget
– Show one neat technical thing that came out of
your work. Could be:
• A surprising example
• A design idea
• A formalism
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Last Third
• Evaluation
– Explain how you evaluated your work
– Present your results
• Conclude
– Summarize your conclusions with one key
point
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Some Specific Advice
• Average 2 minutes per slide
– Simple slides: big text, use pictures, less words
than this slide
• Figure out what the most important 1 or 2
sentences in your talk are and memorize
them
• Practice
– I will listen to any group that wants to practice their
talk Sunday night; email your preferred time 6pmmidnight.
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Giving good talks is one of the
most important ingredients of
a research career.
Take advantage of all
opportunities you can to
practice!
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Programming
• Everything we have seen
so far looks identical to
my Grandma:
– Geeky engineer sits at a
keyboard
– Types a bunch of cryptic
instructions giving step-bystep way of solving a
problem
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Declarative Programming
• Prolog (Programmation en Logique)
[Colmerauer and Roussel, 1972]
– Designed for natural language processing
– Used in AI community
• Program is a database of facts and inference
rules, executes by proving or disproving
conjectures
• Separates logic from control:
– Logic – requirements of a satisfactory answer
specified by programmer
– Control – how to get it (mostly hidden in interpreted)
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PROLOG example: facts
parentOf (chelsea, bill)
parentOf (chelsea, hillary)
parentOf (bill, roger)
parentOf (bill, virginia)
female (chelsea)
These may look like
female (hillary)
function calls, but they are
female (virginia)
just axioms.
male (bill)
male (roger)
occupationOf (chelsea, student)
occupationOf (roger, used_car_salesman)
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Interactive Session
?- parentOf (chelsea, bill)
Yes
?- parentOf (chelsea, roger)
No
?- parentOf (chelsea, X)
X = bill;
X = hillary;
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Defining Rules
• We can define a predicate in terms of other
predicates:
motherOf (X, M) :parentOf (X, M), female (M)
• Read: “M is the motherOf X if M is the
parentOf X and M is female.”
?- motherOf (chelsea, Z)
Z = hillary;
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Combining Predicates
grandMotherOf (X, G) :parentOf (X, M), motherOf (M, G)
ancestorOf (X, A) :- parentOf (X, A)
ancestorOf (X, A) :parentOf (X, P), ancestorOf (P, A)
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Evaluation
• Uses unification algorithm like in type
inference: guess all possibilities exhaustively
?- ancestorOf (chelsea, X)
X = bill;
PROLOG specifies rules
X = hillary;
tried in order.
X = roger;
X = virginia;
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PROLOG Factorial
factorial (0,1)
factorial (N,F) :N > 0, factorial (D, R),
D is N - 1, F is N * R.
?- factorial (5, F)
F = 120;
?- factorial (N, 27)
no
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Programming by Example
• Sometimes called “Programming by
Demonstration”
• Programmer demonstrates task, system
records
• Widely used instance:
– Word/Excel/PowerPoint Macros
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Stagecast Creator
• Started as KidSim Project, Apple
• Before-After Rules:
– Guard: predicate to match before invoking
rule
– Action: sequence of primitive operations
Demo at end of class (time permitting)
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ToonTalk
• Provide a video-game metaphor syntax
to a regular programming language
• Variables = Boxes
• Passing Parameters = Birds
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ToonTalk Metaphors
• Comparison = Scale
• Robots act like guarded commands
– Thought bubble is predicate
– Sequence of actions after matching
Demo at end of class (time permitting)
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Visual Programming
A favorite subject for PhD dissertations
in software engineering is graphical, or
visual, programming - the application of
computer graphics to software design....
Nothing even convincing, much less
exciting, has yet emerged from such
efforts. I am persuaded that nothing will.
Fred Brooks, 1987
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Amorphous Computing
Cement
10 GFlop
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Premise
• Will soon be cheap to make lots of
computational stuff so long as:
– It doesn’t all have to work correctly
– Arrangements and interconnects are mushy
• Examples:
– Construct logic circuits using biological cells
– Nanotechnology
• How to you program it?
– Get acceptable answers with high probability
– Look to biology for inspiration and metaphors
• Human genome: 3 Billion base pairs = 750 MB
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Model
• Computing elements sprinkled on a
surface or in a volume
• Each can talk to a few nearby
neighbors, but not reliably
• Each has modest computing power and
a modest amount of memory
• The particles are not synchonized, nor
are they regularly arranged.
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Slide adapted from Abelson/Knight/Sussman talk
Why is this interesting?
•
•
•
•
Physically feasible at any scale
Forces robustness of design
Potentially extremely inexpensive
Provides the possibility of bulk
computation
– smart paints
– smart gels
– concrete by the Megaflops
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Slide adapted from Abelson/Knight/Sussman talk
Amorphous Computing Medium
An amorphous medium has independent computational
particles, all identically programmed. Each particle is
represented by a spot in the picture, and different states
are shown by different colors.
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Biological Programming
• Growing-Point Language [Coore99]
• Two primitive datatypes:
– Materials – colored stuff that can be left in
amorphous medium
– Pheromones – chemicals that attract or
repel other growing points, spread out
diffusely when secreted
• Growing points may split, die, merge
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Growing Point Program
(define-growing-point (A-to-B-segment)
(material A-material)
What is deposited as it grows
(tropism (ortho+ B-pheromone))
Grows towards increasing quantities
of B-pheromone.
(actions
(when ((sensing? B-material) Stop when touching
B-material.
(terminate))
(default (propagate)))))
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Growing Point Program
(define-growing-point (B-point)
(material B-material) What is deposited as it grows
No tropism – it is stationary.
(for-each-step
(secrete 10 B-pheromone)))
Distribute B-pheromone to neighbors,
radiates approximately symmetrically,
decreasing over 10 units.
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Slide adapted from Abelson/Knight/Sussman talk
Start with Vdd, Vss, and a Poly
Contact
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Slide adapted from Abelson/Knight/Sussman talk
The poly contact sprouts a growing point that
bifurcates and then grows toward the
pheromones secreted by Vdd and Vss.
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Slide adapted from Abelson/Knight/Sussman talk
When the growing poly gets close to Vdd and
Vss it is stopped by a short-range inhibition
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Slide adapted from Abelson/Knight/Sussman talk
The poly growing points die off, but first they
sprout P and N transistor diffusion growing
points, which grow toward Vdd and Vss, where
they drop contacts.
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Slide adapted from Abelson/Knight/Sussman talk
The diffusions also grow toward each other.
When they hit they form a new poly contact and
poly growing point.
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Slide adapted from Abelson/Knight/Sussman talk
The process then repeats, growing the next
inverter.
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Slide adapted from Abelson/Knight/Sussman talk
This process repeats to make an arbitrarily long
chain of ugly, but topologically correct inverters.
This demonstrates totally local control of precision topology.
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Slide adapted from Abelson/Knight/Sussman talk
(define-growing-point
((poly from-input-contact) Q-id)
(material poly)
(tropism constant Vdd-long Vss-long)
(initialize lifetime 5)
(secrete (poly-short inhibitor) Q-id)
(when (= lifetime 0)
(start-growing-point (poly up) Q-id)
(start-growing-point (poly down) Q-id)
(terminate)))
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Amorphous Computing
Summary
• If you have enough randomly-distributed
amorphous stuff, can use GPL to
produce any topology
• We can build arbitrary logic out of
biological stuff (DNA, RNA)
– Once you can make an inverter, can
program anything
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Which of PROLOG,
ToonTalk, Creator, GPLs
are Programming
Languages?
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What is a language? (Lecture 1)
Nerdy Linguist’s Definition of Language:
A description of pairs (S, M), where S stands for
sound, or any kind of surface forms, and M stands
for meaning. A theory of language must specify the
properties of S and M, and how they are related.
Liberal Definition of Programming
Language:
A language (according to the nerdy
linguist’s definition) intended to be read and
written by humans and processed by
machines.
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From Lecture 2:
• Bruce MacLellan’s definition:
A language that is intended for the
expression of computer programs and
that is capable of expressing any
computer program.
• Ravi Sethi’s definition:
Notations used for specifying, organizing,
and reasoning about computations.
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Charge
• Tomorrow, 11:59pm – Project Reports
due
• Monday night: Rotunda Presentations
• Next time: Jeopardy (in your project
teams)
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