AI Expert Newsletter
AI - The art and science of making computers do interesting
things that are not in their nature.
I'm happy to welcome Jocelyn Paine as a guest editor for this month's
Welcome to the November 2004 Newsletter. Dennis has asked me to
take over a few issues, so I'd like to introduce myself. Like Dennis,
I like Prolog, and have used it as my language of choice for various
applications, including expert systems, analysing financial data,
and teaching AI at Oxford University. My most recent project is
an algebraic manipulation system for safe spreadsheet construction,
to try and reduce some of the billions of pounds lost to spreadsheet
errors every year. Having declared a bias to Prolog and logic-based
AI, I must add that this is not the only way to go. I've worked,
and in some cases fought, with a variety of other languages, and
with AI topics ranging from machine learning and artificial life
to interactive fiction and drug design. As Dennis said in his welcome
to the Premier Issue of the Newsletter, AI encompasses a wide variety
of fascinating software and hardware. There's tons of interesting
stuff out there, and I hope I'll keep you informed and entertained.
If there's anything you'd like to see, do please send me your suggestions,
You can learn more about Jocelyn and his interests at his Web site:
I could not resist beginning an AI Newsletter with this quote:
Despite all our propaganda attempts to convince you otherwise,
AI is alarmingly easy to produce; the human brain isn't unique,
it isn't well-tuned, and you don't need eighty billion neurons joined
in an asynchronous network to generate consciousness. And although
it looks like a good idea to a naive observer, in practice it's
Anyone who has wasted a day in fruitless search for a typo or
absent-mindedly loaded their morning biscuit into the floppy drive
knows the human brain is not well-tuned. And we all hope AI is easy
to produce. But deadly? I thought I'd begin this issue with some
fiction, to stimulate the sense of wonder and restore the vigour
on those days when we feel that never mind solving strong AI, it'll
be a miracle if we can even get
append to work correctly.
The quote is from the short story Antibodies by science fiction
writer Charles Stross,
in which AI is so feared that the protagonists take some very
extreme measures to avert its development:
"Damn it, Bob, I really had high hopes for this world-line.
They seemed to be doing so well for a revelatory Christian-Islamic
line, despite the post-Enlightenment mind-set. Especially Microsoft
"Was that one of ours?". She nodded.
"Then it was a master-stroke. Getting everybody used to exchanging
macro-infested documents without any kind of security policy. Operating
systems that crash whenever a microsecond timer overflows. And all
The story begins when a computer programmer is notified that all
NP-complete problems lie in P, the travelling salesman problem can
be solved in O(n2), and thus computer security is forever
compromised. Via a sleeper trip to Edinburgh - in this timeline,
neither the capital of the Viking Empire, an active volcano, nor
a cloud of feral nanobots - and interrogation by police officers
playing cheesy psychedelic videos in an attempt to perform a buffer-overflow
attack on their limbic systems, the protagonists realise their attempts
to cripple computing technology have failed. Even now, artificial
intelligence programs are exploiting the NP-complete-is-P discovery
to optimise themselves. And these AIs are not nice AIs. They want
to take over the Internet and then all the humans, before they eat
the entire Universe, converting it into a kind of ubiquitous computational
dust in their exponential lust for processing power. Will they succeed?
Read the story, and enjoy the amusing little asides, to find out.
Unless you imagine yourself as the nascent AI, Antibodies
is not an optimistic story. The Atrocity Archive, which originally
appeared in Spectrum SF and is now published in book form, is. It's
also an excellent, and funny, combination of secret service novella
and computer geekery with the Cthulhu
mythos of H P Lovecraft. The main character, when not busy maintaining
his department's Beowulf clusters, frankenstein servers and tarot
permutators, perusing Knuth's blacklisted 4th volume to the Art
of Computer Programming, or attending departmental training
"We're going to try a lesser summoning, a type three
invocation using these coordinates I've sketched out on the blackboard.
This should raise a primary manifestation of nameless horror, but
it'll be a fairly tractable nameless horror as long as we
observe sensible precautions. There will be unpleasant visual distortions
and some protosapient wittering, but it's no more intelligent than
a News of the World reporter - not really smart enough to
be dangerous. Manesh, if you could switch on the ABSOLUTELY NO ENTRY
- manages to avert a Nazi invasion which, if not prevented, would
rage a wind of desolation and pain through the heart of Europe before
plunging the Earth into an eternal fimbulwinter. He also, and this
is the really important bit, saves himself from the clutches of his
departmental administrator. (I once had an administrator like that,
and it wasn't nice. Stross has clearly suffered too. It is clear that
he has also lived amongst computer geeks.)
And finally, another chunk of optimism. All the stories mentioned
are fun in the way they play with computing and AI, but this is
the most relevant to us. As Stross says at www.accelerando.org/,
his Accelerando novellas (due out in book form in July 2005)
explore the human consequences of the technological revolution,
by following three generations of a family through the technological
singularity. The quote below is from one novella in the series,
which first appeared in Asimov's Science Fiction and was nominated
for the Hugo award:
Welcome to the third decade. The thinking mass of the
solar system now exceeds one MIP per gram; it's still pretty dumb,
but it's not dumb all over. The human population is near maximum
overshoot, pushing nine billion, but its growth rate is tipping
towards negative numbers, and bits of what used to be the first
world are now facing a middle-aged average. Human cogitation provides
about 1028 MIPS of the solar system's brainpower. The
real thinking is mostly done by the halo of a thousand trillion
processors that surround the meat machines with a haze of computation
- individually a tenth as powerful as a human brain, collectively,
they're ten thousand times more powerful, and their numbers are
doubling every twenty million seconds. They're up to 1033
MIPS and rising, although there's a long way to go before the solar
system is fully awake.
Now that's what I want to see. Leave the AI's to do the real
thinking, and let the organic bits do what they do best - wibble on
about food, sex and football.
- Stross's main site, including information on his fiction and where
to buy it. He has a few short stories on-line. There are also links
to his computer journalism - articles from Computer Shopper,
writings on Linux and Perl - and his weblog.
- Another Stross site, devoted to Accelerando. Headed by
a US Commission on National Security quote about the accelerating
pace of technological change, it explains the motivation behind
the stories, and ends with links to nanotechnology, mind-to-computer
uploading, and other hi-tech topics.
- Review of Antibodies and other stories in Toast.
- Review of The Atrocity Archives.
- Online text of Halo, at Asimov's SF.
- What does the discovery that starts Antibodies, all NP-complete
problems lie in P, mean? Here, from Wikipedia, is an explanation
of P, NP, and why they are so important; and hence, of why some
tasks have no efficient algorithm.
Robots Amongst the Roaches
- Insect telepresence and the Toy Robots Initiative
In a recent weblog entry, Stross writes that he's treating himself
to a new PDA for his birthday. It will have twice the memory of
his old server, be 50% faster, and be upgradeable to 10Gb of flash
memory. And this little sardine-tin sized thingy holds as much power
as a budget server did in 2000, or a fast mid-range Sun box in 1994,
or a supercomputer that would have left Seymour Cray drooling a
decade earlier. As Stross says, "Yesterday's supercomputer is truly
With this in mind, I thought I'd see what's new in robot toys.
They've long seemed an ideal setting to develop robotics: competitive,
fun, and not too demanding. You might have specced your company's
robot cat to recognise its owners' faces from 2 meters after only
3 exposures, but you won't be sued if it fails to, and anyway, some
hobbyist will eventually work out how, then publish their code on
a toy-hacker's site. Nor is the average toy big enough to run amok
and eat the baby, so safety need not be a big concern. However,
my first search landed me not at home, but amongst a herd of Madagascan
hissing cockroaches at the Carnegie Museum of Natural History.
Within its many exhibits, the museum has live exhibitions of exotic
insects. These are as fascinating as any product of evolution, but
many visitors fail to appreciate them, as frequent "Ew Gross" comments
in the museum visitors' book testify. Providing staff to guide visitors
around the exhibitions can help, but this is expensive. Anyway,
even with a guide, you can't get close enough to the insects to
see the complexity of their anatomy and behaviour.
To solve this, the Toy
Robots Initiative at Carnegie Mellon University has worked with
the museum to develop a tele-embodiment robot that visitors can
drive amongst the insects, in effect shrinking down to insect size
and meeting them eyeball to compound eye. Tele-embodiment is the
use of robotics to involve a human in a new environment in a way
that makes them feel they're actually there. Because of the size
difference betwen insect and human, the TRI call this project, "tele-embodiment
It's interesting that in this project, less was more. The TRI
Telepresence paper describes as well as the robot's construction,
the "Contextual Inquiry and Design" used to determine how the robot
should interact with its users (and, in this case, also with the
insects). This, eliciting the human-computer interaction requirements,
is an essential first stage in design. One requirement that came
out of this analysis is that the robot must not be able to harm
the insects - some visitors would inevitably try to drive over and
squash them, were this possible. Consequently, the designers opted
not for a standalone mobile robot but for a camera running on tracks
above the insects, capable of being moved around the enclosure and
rotated. Certainly, were the robot fully mobile, it would seem difficult
to program it to ignore user commands to damage the insects.
Three short videos from the robot can be downloaded from the links
below. In the paper - written probably 1999 - the authors state
that, after user-testing a prototype in the CMU Mobot Programming
Lab, a version was installed as a permanent exhibit near the museum's
entrace. It's a nice idea, worth considering for similar exhibits
elsewhere. One amusing point to emerge from the user testing was
that about 1/5 of the users liked just driving the robot round and
round without paying much attention to the insects. As the designers
Our theory for this action is that the users are engaged
by the motion. The color monitor is extremely large, and so continuous
motion is visually stunning to the point that motion sickness is
possible. Those behaving this way seem to spend little time actually
looking at the bugs, and more time driving like a video game.
- Toy Robots Initiative home page. This links to the Insect Telepresence
Project, as well as the TRI's other work.
- the Insect Telepresence paper, as PDF.
- Three short videos of cockroaches taken by the insect telepresence
robot, as Quick Time.
Artificial evolution is itself evolving to a point where its products
can be as fascinating as those of biological evolution. If the Insect
Telepresence project can immerse us in the natural, Karl Sims's
work can, with its emphasis on art as well as technology, involve
us in the beauty of the artificial.
I came across Sims ten years ago, at an artificial life conference
in Brighton. His presentation showed videos of a simulated beachside
world - complete with a physics engine which implemented both gravity
and friction - in which he had placed an initial population of several
hundred simple blocklike creatures with randomly-generated bodies
and nervous systems. Each creature was tested for its ability to
perform a pre-specified task. Those that were most successful survived,
and their genes were copied, combined, and mutated to make offspring
for a new population. The new creatures were once again tested,
and so evolution continued.
One of the tasks set for the creatures was to move as far as possible
in a certain direction. Members of the first generation might only
twitch feebly or stand like sticks, but as simulated generations
were born and died, some impressive results emerged. I remember
a helical snake which had evolved to gain purchase on the ground
by friction, whilst moving forward by spiralling along its axis.
In another task, the creatures had to evolve to gain and keep
possession of a large virtual cube at the water's edge. One creature
evolved huge flippers for this purpose, one of which it wrapped
around the cube to protect it, whilst bashing competitors to pieces
with the other. There are some pictures of these creatures at Sims's
Virtual Creatures page. No videos unfortunately - it seems the
volume of downloads overwhelmed the server.
How do these creatures work? Sodarace
is a joint venture between the Soda arts company and Queen Mary
College, which enables users to build virtual creatures from simulated
springs, muscles and other body parts and then race them against
competitors in the Sodarace application. There's lots of documentation,
and it's well worth exploring. I mention it here in order to cite
an interview with
Ed: Your Evolved Virtual Creatures are highly successful
not only at excelling in their virtual domains, but also as beautiful
creations inspiring many to contemplate and explore artificial evolution.
Do you have three golden rules for our users that are going to be
writing computer programs to generate and optimise racing Sodaconstructor
Karl: The main secret I think is designing a good genetic language.
You want to be able to describe all possible creatures with some
language that you can then use as your virtual DNA. You want it
high level enough so you could "design" with it - you should be
able to make some successful creatures by hand. You also want random
and mutated codes to have a reasonable chance of being viable. I
think the language should be able to somehow re-use "ideas" by instancing
parts of the creature such as a "leg". Reusable growth rules such
as in L-systems or directed graphs can allow this. But at the same
time you want it to be flexible enough to describe any possible
creature, not just a subset that you've designed into the language.
Finally of course you need to mutate and combine the genetic codes
from different individuals so you get a good balance of viable offspring
and significant changes. I guess that is more than 3 rules, but
there you go.
So what are genetic graphs? Most genetic algorithms represent
genes as linear strings, mimicking chromosomes. However, Sims's
creatures have - as his paper
on Evolving 3D Morphology and Behavior by Competition describes
- graphs or networks for genes. The idea is that the graphs describe
the creatures' shape (the number and connectivity of their body
parts) and at the same time, their neurology. Each body part has
its own neural control circuitry, and the graph node that determines
the part's shape also determines the circuitry. This leads to modularity.
If a mutation causes a body part to be duplicated, say, then the
control circuitry will be duplicated along with it, increasing the
chance that the mutatated individual continues to function in its
environment. Details, and diagrams of graphs and the creatures derived
from them, can be seen in the paper.
There are one or two other papers on genetic graphs available
on the Web. In common with other representations of genes, genetic
graphs can be "direct" or "indirect". Direct genetic graphs not
only act as genes, but also as the creature (the "phenotype") derived
from it. In contrast, indirect genetic graphs have to be transformed
into the phenotype by a "developmental" process - something analogous
to embryonic development. Sims's genetic graphs are indirect - the
creatures are not themselves graphs, but assemblages of blocks.
Another indirect genetic graph system is Jianjun Hu's EvoGraph,
developed at Michigan State University. The site demonstrates its
use for optimum placement of wireless access points, and has some
downloadable C++ libraries. Direct genetic graphs have been used
design. Here, the object being evolved - the molecule - is
the gene. The links below include a paper on this work, and also
account written for drugs designers.
As Sims explains in his Sodarace interview, the motivation for
the evolved virtual creatures came from his computer art. In Panspermia
- named for the theory that life is distributed throughout the universe
in the form of germs or spores - Sims created a short and rather
lovely animation in which spores land on a barren planet, germinate,
and slowly unfurl into graceful and elaborate fernlike plants, which
then seed and, cannon-like, blast their own spores back into outer
space to continue the cycle. Sims needed a practical way to create
these artificial plants, began experimenting with artificial evolution,
and then went on to evolve creatures not for their shapes but for
After the virtual creatures work, Sims turned back to art, so-called
aesthetic evolution. Here, it's the human rather than the computer
that provides the selection pressure. We start with a population
of randomly generated art works - images, music, or whatever - and
at each generation, the user selects those most desired to pass
on for further evolution. The Galápagos
page shows this at work in an interactive media installation exhibited
at the ICC in Tokyo between 1997 to 2000. As Tom Ray, the inventor
of the Tierra
artificial life system, writes in an essay on evolution
Evolution is a creative process, which acting independently,
has produced living forms of great beauty and complexity. Today,
artists and engineers are beginning to work together with evolution.
In the future, it may be possible for artists to work in collaboration
with evolution to produce works of art whose beauty and complexity
approach that of organic life.
Many, many people do not believe anything as complex as a living
creature can arise by evolution. The first example I turned up on
the Web was an "intelligent
design" site - "Intelligent Design, (I.D.) shows the incredible
complexity of the world's creatures and it provides compelling evidence
that they have been created rather than randomly evolved". Sims's
virtual evolved creatures, which modern PCs are surely powerful
enough to run, demonstrate that evolution is sufficient, and would
make a wonderful educational tool. I commend them to any game designer
looking for a new product.
Let's end with another quote, from an interview Sims gave Wired:
Did transferring evolution into a computer diminish Sims's
appreciation for the wonders of biological life? Sims says that
the projects have actually increased his respect for living things:
"Exposing more details about life and how it might have occurred
makes it even more amazing. You can't help but appreciate the extreme
unlikeliness that any given organism or species ever evolved at
- Karl Sims's home page at Genarts. This links to other pages on
his work, with copious pictures, including the Evolved Virtual Creatures
photos, Panspermia and Galápagos.
- Sodarace home page. The interview is at http://sodarace.net/sims/index.jsp.
- Evolving 3D Morphology and Behavior by Competition, as
- Jianjun Hu's EvoGraph genetic graph system.
- Automatic molecular design using evolutionary techniques.
- Survival of the Fittest in Drug Design. This article is
on the Modern Drug Discovery site, and gives an easy-to-read
summary of the above paper, from the point of view of a drug designer.
- Do-It-Yourself Darwin - Karl Sims invites you to play God among
the machines. This is the text of the Wired interview.
As it says, "In Sims's virtual biosphere, boredom equals death.
It's survival of the aesthetically fittest".
- A paper by Tom Ray, the inventor of the Tierra artificial life
system, on evolution as artist. Tom's Tierra home page is at http://www.his.atr.jp/~ray/tierra/index.html.
- One example from many of a site whose author does not believe
evolution created life.
Although Prolog makes some programs remarkably concise, it's less
than ideal when we want to write expressions containing nested function
calls. The programmer has to unwrap these into sequences of goals,
giving code which is verbose and hard to read. In this Prolog Code
Corner, I show how to overcome this by writing a preprocessor for
a functional dialect of Prolog. Note that here, "functional" has
its usual computer science meaning of "programmed in terms of function
calls" rather than "working". All my programs work.
Let us suppose that Prolog's designers had never thought of
for doing arithmetic, but instead had provided predicates such as
of which carries out an arithmetic operation on its first two arguments
and returns the result in the third. Then instead of being able
X is A*B + A*C + A*D.
we would have to code the expression as calls to
mult. This would force us to manually unwind the
expression and invent new variables to hold intermediate results:
mult( A, B, V1 ),
mult( A, C, V2 ),
mult( A, D, V3 ),
plus( V1, V2, V4 ),
plus( V4, V3, X ).
Fortunately, we do have
is, and so we are spared
this inconvenience when doing arithmetic. It still faces us, however,
whenever we want to write expressions for manipulating sets, vectors,
complex numbers; indeed, any time we just want to nest function
calls. In effect, Prolog forces us to become our own compilers.
Even an ancient language like Fortran does better.
My answer to this problem was to write a preprocessor.
This did two main things. It allowed expressions to occur as the
arguments to goals, and generated code that evaluated them and replaced
them by their results. They were signalled by
eval: thus I can say, for example, in
my Prolog source files or to the top-level interpreter:
write( eval(1+2) )
write( eval( length( [1,2,3] ) ) )
and have it mean the same as
write( 3 )
This has marvellous benefits for the brevity of my code.
The preprocessor also understands functional definitions. These
are indicated by the connective
<-, analogous to
:-. Using this, I can write code such as
factorial( 0 ) <- 1.
factorial( N ) <- N * factorial( N-1 ).
In the rest of this feature, I explain how I did this. Some of the
techniques can be used to extend Prolog in other ways, which may be
useful if you want to implement your own favourite notation for a
The idea is to translate each expression into a pair consisting
of a goal and a "result". The result will be the original expression
if it needs no evaluation, otherwise a variable which the goal will
bind to the result of evaluation. For example:
We can do this very simply. If the expression is a number, we
return it as the result. The goal doesn't need to do anything, so
we make it
true. Otherwise, we assume the expression
is a function call. We split this into the function, F, and its
arguments. We translate the arguments into a goal AG which evaluates
them, together with variable to hold the result of evaluation. We
then conjoin to AG another goal FG, formed by calling F on a list
of the evaluated arguments with a result variable appended.
Some examples may help. To translate
code is required to evaluate the arguments, so the goal AG becomes
true. The goal FG is the function F -
- called on the evaluated arguments with a result variable appended.
In this case, the evaluated arguments are the same as the originals,
so FG becomes
the result variable.
For the more complicated expression
plus( mult(1,2), 3 ),
the goal AG would become - via a recursive call -
The evaluated arguments would becone
[ R1, 3 ], where
the first one is replaced by the variable holding the result of
evaluating it. The goal FG becomes
plus( R1, 3 ). And
finally, the goal for evaluating the entire goal becomes AG conjoined
with FG, or
mult( 1, 2, R1 ), plus( R1, 3, R )
Here is the translation code. The predicate
translates the expression in its first argument into a result in
its second and a goal in its third. It calls
to translate function arguments:
trans_expr( Expr, Expr, true ) :-
number( Expr ), !.
trans_expr( Expr, Expr, true ) :-
var( Expr ), !.
trans_expr( Expr, ResultVar, Goal ) :-
Expr =.. [ F | Args ],
trans_arglist( Args, ArgResults, ArgGoals ),
append( ArgResults, [ResultVar], ArgsAndResultVar ),
ExprGoal =.. [ F | ArgsAndResultVar ],
Goal = ( ArgGoals , ExprGoal ).
trans_arglist( [ Arg1 | Args ], [ Result1 | Results ], Goal ) :-
trans_arglist( Args, Results, Goals ),
trans_expr( Arg1, Result1, Goal1 ),
Goal = ( Goal1 , Goals ).
trans_arglist( , , true ).
Note that we have added a clause which checks for expressions that
are variables. These are unlikely to occur as the argument to
but will turn up once we start translating definitions, which are
likely to contain variables (possibly from their head) in the tail
expression, in the way that the second clause for
factorial( N ) <- N * factorial( N-1 ).
If you try this code, you will find the generated goals contain
a lot of unnecessary
trues. We can remove these by
writing a special goal-conjoining predicate:
conjoin( true, G, G ) :- !.
conjoin( G, true, G ) :- !.
conjoin( G1, G2, (G1,G2) ).
This is a good utility to have whenever we write programs that code-generate
The Prolog I normally use, SWI,
contains a number of "preprocessor hooks": predicates that you can
define to add your own preprocessing code. These are not in the
ISO Prolog standard, but several other implementations, such as
also provide them. If your Prolog doesn't, you will have to find
another way to hook your preprocessor into it. The easiest is to
write your own version of
I use the
goal_expansion hook to add the
macro mentioned earlier. As it reads a file,
SWI-Prolog hands each goal G appearing in the body of a clause to
goal_expansion, passing it as the first argument. If
the call succeeds, G gets replaced by
second argument, assumed to be its translation.
First, we write a predicate to translate goals whose arguments
may include an
trans_goal( G, GTranslated ) :-
G =.. [ F | Args ],
trans_goal_args( Args, ArgResults, ArgGoals ),
FGoal =.. [ F | ArgResults ],
GTranslated = ( ArgGoals , FGoal ).
trans_goal_args( , , true ) :- !.
trans_goal_args( [Arg1|Args], [Result1|Results], Goal ) :-
trans_goal_arg( Arg1, Result1, Goal1 ),
trans_goal_args( Args, Results, Goals ),
Goal = ( Goal1 , Goals ).
trans_goal_arg( Arg, Result, Goal ) :-
nonvar( Arg ), Arg =.. [ eval , E ], !,
trans_expr( E, Result, Goal ).
trans_goal_arg( Arg, Arg, true ).
trans_goal runs over the arguments of
a goal G. If any argument is a term
trans_expr on the expression E. It collects the
goals for evaluating the arguments and prepends them to G. Thus, the
write( eval( plus(1,2) ) )
gets transformed into
plus( 1, 2, R ), write( R ).
Notice that the first clause to
to test whether it is dealing with an
We take care not to write the
eval(E) explicitly, instead
=.. to test for it. Were we not to do this,
then if we already had the preprocessor installed (which we might
if repeatedly editing, cnsulting, and testing it), it would try
expanding this particular
eval(E), with amusing, but
probably unhelpful, effects.
Having written the translator, we install it. This involves adding
a clause for
goal_expansion which calls
We also make this clause test whether the goal actually needs translating
- whether it does contain an
eval - and fail if not.
This is good practice, because some Prologs might call
over and over again on the same goal if we make it return the original
without any change. Here is the code:
needs_translating( G ) :-
nonvar( G ),
G =.. [ _ | Args ],
member( Arg, Args ), nonvar( Arg ), functor( Arg, eval, 1 ), !.
:- multifile user:goal_expansion/2.
:- dynamic user:goal_expansion/2.
user:goal_expansion( G, GTranslated ) :-
needs_translating( G ), !,
trans_goal( G, GTranslated ).
trans_goal, we take care to avoid an explicit
eval(E) in the code.
Note that there could be could be several different clauses for
goal_expansion in force at the same time if you or
someone else have installed other preprocessors. Care will be needed
to ensure these work correctly together, especially if a single
goal contains constructs from different preprocessors, or gets translated
by one preprocessor into a construct handled by another.
Translating function definitions is now straightforward. To translate
double(N) <- plus(N,N).
plus(N,N), giving the goal
We use this as the tail of the new predicate. We then insert
as the final argument of
double( N, R ) :- plus( N, N, R ).
Here is the code:
:- op( 1200, xfx, user:(<-) ).
trans_def( (Head <- Expr) , (PrologHead:-Tail) ) :-
trans_expr( Expr, ResultVar, Tail ),
trans_head( Head, ResultVar, PrologHead ).
trans_head( Head, ResultVar, PrologHead ) :-
Head =.. [ F | Args ],
append( Args, [ResultVar], ArgsAndResultVar ),
PrologHead =.. [ F | ArgsAndResultVar ].
Finally, we install
trans_def using the
preprocessor hook. This resembles
translates complete terms in the source file. For us, it is simpler
to use: we can test whether a definition needs translating just
by whether it contains a
<- connective; we don't
need to decompose definitions in the same way we did with goals;
and we don't need to worry about avoiding explicit calls to
in the translator. Here is the code:
:- multifile user:term_expansion/2.
:- dynamic user:term_expansion/2.
user:term_expansion( Def, DefTranslated ) :-
trans_def( Def, DefTranslated ).
The translator above is pretty basic, and would need extending
to be useful. For example, the atom
 (empty list)
probably wants to be treated as a constant that stands for itself
in the same way as the numbers; non-empty lists almost certainly
should be evaluated element by element.
Other constructs that could be added include an expression-disjunction
operator. If we call this
E1, return its result if it succeeds,
and otherwise return that of
Prolog has such a thing. Along different lines, it's probably
* and other arithmetic
operators into calls to
is. My preprocessor does this,
enabling me to use them in the definition of
We need to take care with the semantics. One question is how to
distinguish between atom constants and functions of no arguments.
For example, how should we invoke
read/1 as a function?
We can't write
read(), because it's not valid syntax.
If the translator takes the atom
read to denote a call
to the predicate, how should we write the atom constant
On the other hand, if
read denotes the atom, then we
need a special notation for the function call. In my work, I eventually
decided that an atom on its own should be a call, and that to make
it a literal, we precede by
` (backquote), declared
as a prefix operator. In fact, I use
` to quote any
term, not just atoms.
Another semantic question concerns
eval. Should the
translator recognise it if it occurs at any level inside a goal,
or only at the top level? That is, should the goal
write( pair( eval( plus(1,2) ), 3 ) )
stay as it is, or evaluate to
write( pair( 3, 3 ) )?
Tempting as it can be just to hack, hack, and hack again, such questions
must be considered. And of course, if you are using any preprocessor
for serious development, it is essential to avoid changes that break
source code already written for it!
My own preprocessor is named GRIPS; a version for SWI-Prolog can
be found at www.j-paine.org/grips.html.
It has a number of enhancements beyond the basic code given here,
including conditionals, options for specifying evaluation modes
for special functions, and a
where operator for embedding
I use functional notation as frequently in my Prolog projects
as I do DCGs, indeed probably much more. Firstly, it just is convenient
when writing complicated expressions. It also saves thinking up
names for result variables, and hence helps mitigate the psychological
condition known as "naming fatigue" - a steadily increasing inability,
as the day wears on, to invent meaningful identifiers. (This is
not meant to be facetious. I once read a book on programming which
asserted that many programmers suffer from naming fatigue.)
Functional notation is also useful because it makes data flow
explicit. When reading a goal such as
bar( C, D, A ), foo( A, B ), fred( B, D )
one has to examine the predicate definitions and accompanying comments
or mode declarations before being sure of the data flow. The same
call in functional notation makes the flow immediately clear:
fred( foo( bar(C,D) ), D )
For this reason, I have experimented with functional notation in teaching
Prolog, to make it easier for novices to read Prolog code.
I am also using GRIPS as the basis of a computer-algebra-style
interface for manipulating spreadsheets. This works in the same
way as Mathematica, Maple, and other algebra programs, except that
the objects handed on from function to function are spreadsheets
and parts of spreadsheets rather than numbers and algebraic expressions.
Functional notation is useful for both: typically in an interaction,
the user puts one or more things in, carries out a transformation,
and gets one thing out which is then either stored for future use
Finally, I have experimented with the idea that dynamic
Web pages can be regarded as functions generating HTML, and
should be written this way, so that all variables referenced on
the pages are explicitly passed as function arguments. This aids
readability, and also makes it simple to include parts of a page
inside one another, since the includes just become function calls.
This is something that Java Server Pages and other popular Web-page
generators do very inelegantly.
- Source and documentation for the GRIPS preprocessor.
- Functional notation in Ciao Prolog, an open-source next-generation
Prolog developed at the Computational Logic laboratory at the Universidad
Polit&eeacute;cnica de Madrid. See http://clip.dia.fi.upm.es/Software/Ciao/
for the Ciao home page.
- The Watchmaker And The Spreadsheet Factory, a demonstration
of spreadsheet manipulation in Prolog. Some of the examples demonstrate
the use of functional notation.
- Dynamic Web pages in Kawa. This implements the idea of
Web pages as functions, using the Kawa dialect of Scheme. A Prolog
version is still under development.
Past newsletters are available at either www.ddj.com
As ever, interesting links and ideas for future issues are very
welcome. Feel free to contact either myself (below) or Jocelyn <email@example.com>
with comments, thoughts and suggestions.
Until next month,
Copyright ©2004 Amzi! inc., CMP, and Jocelyn Paine. All Rights