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Dr. Richard S. Wallace |
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A.L.I.C.E. Artificial Intelligence Foundation, Inc. |
A.L.I.C.E. is an artificial intelligence natural
language chat robot based on an experiment specified by Alan M. Turing in
1950. The A.L.I.C.E. software utilizes AIML, an XML language we designed for
creating stimulus-response chat robots.
Some view A.L.I.C.E. and AIML as a simple extension
of the old ELIZA psychiatrist program. The comparison is fair regarding the
stimulus-response architecture. But the A.L.I.C.E. bot has at present more
than 40,000 categories of knowledge, whereas the original ELIZA had only
about 200. Another innovation was provided by the web, which enabled natural
language sample data collection possible on an unprecedented scale.
A.L.I.C.E. won the Loebner Prize, an annual Turing
Test, in 2000 and 2001. Although no computer has ever ranked higher than the
humans in the contest she was ranked "most human computer" by the two
panels of judges. What it means to "Pass the Turing Test" is not so
obvious. Factors such as the age, intellect and expectations of the judges
have tremendous impact on their perceptions of intelligence. Alan Turing
himself did not describe only one "Turing Test." His original imitation
game involved determining the gender of the players, not their relative
humanness.
The model of learning in A.L.I.C.E. is called
supervised learning because a person, the botmaster, plays a crucial role.
The botmaster monitors the robot's conversations and creates new AIML
content to make the responses more appropriate, accurate, believable, or "human,"
or whatever the botmaster intends. We have developed algorithms for
automatic detection of patterns in the dialog data. This process, called "Targeting,"
provides the botmaster with new input patterns that do not already have
specific replies, permitting a process of almost continuous supervised
refinement of the bot.
Some have argued that Turing, when he predicted that
a machine could play his game in "50 years" after his 1950 paper,
envisioned something more like a general purpose learning machine, which
does not yet exist. The concept is simple enough: build a robot to grow like
a child, able to be taught language the way we are. In our terms, the role
of the botmaster would be fully automated. But even a child does not, or at
least should not, go forth into the world, unprotected, to learn language
"on the street," without supervision.
Automatic generation of chat robot questions and
answers appears likely to raise the same trust issues forced upon the
abandoned child. People are simply too untrustworthy in the "facts" that
they would teach the learning machine. Many clients try to deliberately
sabotage the bot with false information. There would still have to be an
editor, a supervisor, a botmaster or teacher to cull the wheat from the
chaff.
The brain of A.L.I.C.E. consists of roughly 41,000 rules called categories. Each category combines a question and answer, or stimulus and response, called the "pattern" and "template" respectively. The AIML software stores the patterns in a tree structure managed by an object called the Graphmaster, implementing a pattern storage and matching algorithm. The Graphmaster is compact in memory, and permits efficient pattern matching time.
Susan Sterrett?s careful reading of Turing?s 1950
paper reveals a significant distinction between two different versions of
what has come to be known as the Turing Test (Sterrett 2000). The first
version, dubbed the Original Imitation Game (OIG), appears on the very first
page of Computing Machinery and
Intelligence (Turing 1950). The OIG has three players: a man (A), a
woman (B), and a third person (C) of either sex. The third player (C) is
called the interrogator, and his function is to communicate with the other
two, through what would nowadays be called a text-only instant messaging
chat interface, using two terminals (or today perhaps, two windows) labeled
(X) and (Y). The interrogator must decide whether (X) is (A) and (Y) is (B),
or (X) is (B) and (Y) is (A), in other words which is the man and which is
the woman. The interrogator?s task is complicated by the man (A), who
Turing says should reply to the interrogator with lies and deceptions. For
example, if the man is asked, ?are you a man or a woman?,? he might
reply, ?I am a woman.?
Putting aside the gender and social issues raised by
the OIG, consider the OIG as an actual scientific experiment. Turing?s
point is that if we were to actually conduct the OIG with a sufficiently
large sample of subjects playing the parts of (A), (B), and (C), then we
could measure a specific percentage M of the time that, on average, the
interrogator misidentifies the woman, so that 100-M% of the time she is
identified correctly. Given enough trials of the OIG, at least in a given
historical and cultural context, the number M ought to be a fairly
repeatable measurement.
Now, as Turing said, consider replacing the man (A)
with a computer. What would happen if we tried the experiment with a very
simple minded program like ELIZA? In that case, the interrogator (C) would
identify the woman correctly (nearly) 100 percent of the time, so that M=0.
The ELIZA program would not do well in the OIG, but as the variety and
quality of machine?s responses begin to approach those of the lying man,
the measured percentage of incorrect identification ought to be closer and
closer to the M measured with the man playing (A).
Much later in the 1950 paper, in section 5, Turing
describes a second game more like the concept of a ?Turing Test? as most
engineering schools teach it. The setup is similar to the OIG, but now
gender plays no role. The player (B) is called ?a man? and the player
(A) is always a computer. The interrogator must still decide whether (X) is
(A) and (Y) is (B), or (X) is (B) and (Y) is (A), in other words which is
the man and which is the machine. Sterrett calls this second game the
Standard Turing Test (STT).
Whole academic conferences have been devoted to
answering the question of what Turing meant by the Turing Test. In a radio
interview taped by the BBC, Turing describes a game more like the STT, but
in the paper he gives more prominence to the OIG. Unlike the OIG, the STT is
not a good scientific experiment. What does it mean to ?pass? the STT?
Must the interrogator identify the machine correctly 50% of the time, or
100%? For how long must the machine deceive the interrogator? Finally, does
the interrogator know in advance that he is trying to ?out?(Zdenek 2000)
the robot, or that one of the players is a machine at all?
Unfortunately the STT, though flawed as an experiment, has come to be popularized as the modern ?Turing Test.? The STT is the basis of real-world Turing Tests including the Loebner Prize, won by A.L.I.C.E. in 2000 and 2001. Although she performs well in STT style contests, the A.L.I.C.E. personality is actually designed to play the OIG. She is a machine, pretending to be a man, pretending to be a woman. Her technology is based on the simplest A.I. program of all, the old ELIZA psychiatrist.
The story of Joseph Weizenbaum is in many ways almost as interesting as that of Alan Turing, although not so tragic. One almost pictures Weizenbaum as a latter-day Luddite, smashing artificial intelligence the way British tradesmen smashed factories in reaction to the Industrial Revolution. But unlike the Luddites, Weizenbaum himself was the inventor of the machine. It would be more akin to the Wright Brothers destroying their prototype airplane after its first flight, saying ?this technology is too dangerous. We must put the genie back in the bottle.?
A typical conversation with ELIZA is the following:
C: Well, my boyfriend made
me come here.
R: Your boyfriend made you
come here.
C: I am unhappy.
R: Do you think coming
here will help you not to be unhappy?
C: I need some help, that
much seems certain.
R: What would it mean to
you if you got some help?
C: Perhaps I could learn
to get along with my mother.
R: Tell me more about your
family.
C: You are like my father
in some ways.
R: What resemblance do you
see?
C: My father is afraid of
everybody.
R: What else comes to mind
when you think of your father.
C: Bullies.
R: Does that have anything
to with the fact that your boyfriend made you come here?
Note: In all sample dialogues following, the ?R:? stands for ?Robot:? and the ?C:? stands for ?Client.?
One of ELIZA?s fundamental ?tricks? is the
pronoun reversal exhibited in the first exchange. Mapping ?my? to ?your?
and ?me? to ?you,? the robot echoes the client input by exchanging
first and second personal pronouns. Other replies are generic defaults,
giving the illusion of some understanding. If the client says anything with
the prefix ?I need some X,? the robot replies with ?What would it mean
to you if you got some X?? It doesn?t matter whether X is help, money,
food, water, love or time. The same answer will cover almost all the likely
inputs.
Still other ELIZA replies are based on simple keyword
recognition, as in the exchange about the client?s mother, when the robot
says, ?Tell me more about your family.? The appearance of the keyword
?mother? anywhere in the input may have triggered this response. ELIZA
has a limited memory of the conversation state, as well. When confronted
with the unrecognized input ?Bullies,? she responds by raising the
previously stored subject.
As unlikely as it sounds today, Weizebaum pulled the
plug on ELIZA (Weizenbaum 1976). He was horrified that anyone would actually
believe this simple program said anything about intelligence, let alone had
any. Weizenbaum tells us that he was shocked by the experience of releasing
ELIZA, also known as ?Doctor,? for use by nontechnical staff at MIT.
Secretaries and nontechnical staff thought the machine was a ?real?
therapist, and spent hours revealing their personal problems to the program.
When Weizenbaum informed a secretary that, of course, he had access the logs
of all the conversations, she reacted with outrage at this invasion of
privacy. Weizenbaum was shocked that such a simple program could deceive a
naive client into revealing personal information.
What Weizenbaum found especially revolting was that
the Doctor?s patients believed the robot really understood their problems.
Even some psychiatrists seriously believed the robot therapist could help
patients in a constructive way. Weizenbaum?s reaction might be best
understood like that of a Western physician?s disapproval of herbal
medicine, or an astronomer?s disdain for astrology.
The back cover of the paper edition of Weizenbaum?s
Computer Power and Human Reason (Weizenbaum, 1976) gives us a feeling for
the general attitude toward the book at the time of its release:
?Dare I say it? This is the best book I have read
on the impact of computers on society, and on technology, and man?s image
of himself.??Keith Oakley, Psychology
Today
?A thoughtful blend of insight, experience,
anecdote, and passion that will stand for a long time as the definitive
integration of technological and human thought.??American Mathematical
Monthly
?Superb ... the work of a man who is struggling with the utmost seriousness to save our humanity from the reductionist onslaught of one of the most prestigious, and richly funded technologies of our time.??Theodore Piszak, The Nation.
Computer Power
and Human Reason seems a bit quaint today, much the same as Turing?s
1950 paper does. For one thing, Weizenbaum perceived his mission as partly
to educate an uninformed public about computers. Presumably the uninformed
public was confusing science fiction with reality. Most of the book is
devoted to explaining how a computer works, perhaps a necessary primer in
1976, but today more appropriate in a book called something like, ?Computers
for Dummies.?
Two chapters of Computer
Power and Human Reason are devoted to a humanist attack on artificial
intelligence, on ELIZA specifically, and on computer science research in
general.
Most contemporary scholars did not need much convincing that ELIZA was at best a gimmick, at worst a hoax, and in any case not a ?serious? artificial intelligence project. Yet ELIZA is perhaps the most widely distributed program in the history of artificial intelligence. The irony of Joseph Weizenbaum is that by failing to promote his own technology, indeed by encouraging his own critics, he successfully blocked much further investigation into what would prove to be one of the most persistently interesting demonstrations to emerge from the MIT AI Lab.
Susan Client: Do you think President Clinton should
be impeached?
Robot: It depends what you mean by ?thinking.?
Politicians, at least those in our society, never seem to give a straight answer to a question. If a journalist asks a specific question, the politician answers with a ?sound bite? or short, memorized speech which is related to, but does not necessarily answer, the reporter?s question. If asked about schools for example, the politician may reply with a memorized policy statement on education policy. It is as though the response was merely triggered by the keyword ?school? in the question. No creative or spontaneous thinking is required.
One aspect of the art of writing AIML is composing
brief, concise, interesting, grammatically correct and sometimes humorous
default responses, which work in for a wide variety of inputs matching a
single pattern. President Clinton inadvertently revealed this type of
automatic reply when he uttered the famous quote, ?It depends on what the
meaning of ?is? is.? This could be a default response to any input
beginning with the word ?is.? Like the politician?s replies, the
default responses should indicate an understanding of the question, but not
offend anyone.
There is a democratic aspect to A.L.I.C.E. Born out
of frustration about public apathy toward the existing attempts at
artificial intelligence, A.L.I.C.E. is designed to answer the question, what
do people expect an A.I. ought to be able to say? It turns out that there is
a Zipf like distribution of the queries people make to the robot. The
appearance of the Zipf distribution leads to a natural strategy for working
on the problem: write replies for the most common queries first.
Despite the obvious simplicity of this approach, largely borrowed from the ELIZA psychiatrist program, the prevailing prejudice for many years, at least among the academic elite, has derided ELIZA as a ?toy,? too simple to form the basis of a practical natural language understanding system. One school of thought advocated an approach based on limited domains of discourse, in which practical systems might converse about one subject like moon rocks, blocks, or Star Trek. This school would have us lower our expectations for general natural language understanding however. Another school favors development of large scale ontologies of ?commonsense knowledge,? thought to be necessary for understanding everyday language. Still a third school advocates building something like the general learning machine, which can be taught language like a child. It goes without saying that no other school has produced any machine capable of anything close to the performance of A.L.I.C.E. in Turing?s Imitation Game.
Social situations prove awkward for many of us. The
author, challenged by an almost total apathy toward organized sports of any
kind, navigated the minefield of adolescence in America by making use of a
technique he called ?sports talk.? When cornered with the question, ?Did
you see the game last night?,? the author would always reply, ?No, who
won?? Many sports questions, such as ?Who do you want to win the super
bowl?? can be deflected with dry humor like, ?the team that scores the
most points.? When confronted with a question about a specific sports
player, like ?What do you think of Joe Montana this season?,? can be
handled believably with imaginary biographical data, as in, ?I heard his
brother was a boxer.? By remembering the previous utterance, the technique
may even be extended to more involved dialogues:
C: You should have seen
the game last night.
R: What game?
C: Basketball.
R: Sorry I missed it.
Conversations with strangers may be initiated with
appropriately selected ?pickup lines.? Early in the history of A.L.I.C.E.
the bot also initiated conversations by randomly selecting one of these
lines. It was then possible to measure the average dialogue length of
conversations initiated with each line. Although the original data was sadly
lost, the experiment may be easily repeated with the latest generation of
AIML software. In our brief test, the pickup line with the longest average
dialogue length was, ?What is your favorite movie?? Among about 40
pickup lines tested, the lowest ranked was, ?What do you think caused
World War I?? Another poor choice was, ?Are you a man or a woman?,?
which is ironic because this may be the single most often used pickup line
in human online chat (?r u m/f??).
Experience with A.L.I.C.E. indicates that most casual
conversation is ?stateless,? that is, each reply depends only on the
current query, without any knowledge of the history of the conversation
required to formulate the reply. Indeed in human conversation it often seems
that we have the reply ?on the tip of the tongue? even before the
interlocutor has completed his query. Occasionally following the dialogue
requires a conversational memory of one more level, implemented in AIML with
<that>. When asking a question, the question must be remembered long
enough to be combined with the answer. These same remarks are not
necessarily true in situations requiring highly structured dialogue, such as
courtrooms or classrooms. But in the informal party situation human
conversation does not appear to go beyond simple stimulus-response, at least
not very often.
How often do we find ourselves half-listening,
half-thinking about something else, in the midst of a conversation? Offering
the occasional nod, ?uh-huh,? or other gesture indicating understanding,
we put on the illusion of understanding and ?being human.? Questions are
often repeated in informal human chat as well.
C: Where did you go to
college?
R: I told you before, it
was Cambridge.
C: Oh, that?s right. I?m
sorry.
With her unstructured approach to conversations,
A.L.I.C.E. is also capable of the kind of passive-aggressive data collection
characteristic of human conversations. A totally passive data collection
device is like a web guestbook, where there are no constraints placed on the
data collected. The client may write anything in a guestbook. An example of
an aggressive data collection device is a nitpicky form, which may not even
be submitted until every field is filled.
Humans and A.L.I.C.E. can collect a lot of personal information through the use of leading questions in the conversation, such as ?How old are you?? or ?Are you a student?? We call this type of data collection, passive-aggressive, because it combines elements of the passive guestbook with those of the aggressive form. Provided that bot chats with enough clients, the passive-aggressive method can collect a statistically significant amount of client data. Using this type of data collection we have been able to ascertain that about half the clients of A.L.I.C.E. are under 18, for example.
Every experienced professor knows that there is a
Zipf distribution of questions asked by students in class. The single most
common question is universally, ?Will this be on the test?? The lecturer?s
job is like that of a FAQ bot or politician, to memorize the answers to all
of the most commonly asked questions, and even to match an ambiguous
question with one he already knows the answer to. In the rare event that the
student confronts the teacher with a question he cannot answer, the
professor supplies a default response indicating that he understood the
question and may provide an answer at a later time. One good default
response like that is, ?That is not my area of expertise.?
A general downturn in artificial intelligence and
robotics roughly coincided with the end of the Cold War, as governments and
corporations reduced the amont of funding available for this technology. The
?richly funded? field of 1976 became more like a Darwinian struggle for
diminishing resources. One positive outcome was the brief heyday of ?robot
minimalism,? a design philosophy based on low-cost parts, commodity
computers, low-bandwidth sensing, and general simplicity in design and
engineering. It was a moment when Occam?s razor could cut away much of the
needless complexity that had accumulated over the previous decades. Although
robot minimalism subsequently fell out of favor, it became a significant
influence on the development of A.L.I.C.E.
We used to say there was no theory behind A.L.I.C.E., no neural networks, no knowledge representation, no deep search, no genetic algorithms and no parsing. Then we discovered that there was a theory circulating in applied A.I., called Case-Based Reasoning (CBR) that closely resembled the stimulus-response structure of A.L.I.C.E. The CBR cases correspond to the AIML categories.
Every ?PNAMBIC?(acronym) Pay No Attention to that Man Behind the Curtain [from The Wizard of Oz]. Denoting any supposedly fully automated system that in fact requires human intervention to achieve the desired result.??New Hacker?s Dictionary
A.L.I.C.E. was not the original name of A.L.I.C.E.
The first prototype was called PNAMBIC, in tribute to the hoaxes, deceptions
and tricks that have littered the history of artificial intelligence. But
the machine hosting PNAMBIC was already named Alice by a forgotten systems
administrator, so people began to call her ?Alice.? At that point, we
invented the ?retronym?: Artificial Linguistic Internet Computer Entity.
Yet A.L.I.C.E. is possibly the first A.I. technology to embrace this
tradition of deception openly.
The tradition goes back to Baron von Kempelen and his
18th century ?Chess Playing Automaton.? Also known as the ?Strange
Turk,? this device appeared to play decent games of chess against any
human challenger. Kemepelen utilized a standard magician?s trick, opening
first one cabinet door and then closing it, and opening another one, to
reveal the ?mechanism? inside. According to one legend, the empress of
Russia ordered the machine shot, killing the hapless vertically challenged
Polish operator hidden inside.
A book of fiction and poetry, supposedly written by
an A.I. named RACTER, caused a minor sensation upon its release in 1984.
Later proved to be a hoax (Barger 1993), the book (Chamberlain 1978), called
?The Policeman?s Beard is Half Constructed,? by William Chamberlain,
nevertheless speaks to the public?s willingness to suspend its disbelief
about artificial intelligence. Who can blame them? Hollywood, more than
anyone, has done the most to raise the public expectations for A.I. and
robots.
The following example illustrates the flavor of the
stories told by RACTER. ?Bill sings to Sarah, Sarah sings to Bill. Perhaps
they will do other dangerous things together. They may eat lamb or stroke
each other. They may chant of their difficulties and their happiness. They
have love but they also have typewriters. That is interesting.? RACTER was
a PNAMBIC because obtaining these results required considerable human
intervention. At the very least, a human editor reviewed many random
examples, looking for sensible ones like the story above.
According to one A.I. urban legend, apparently not
documented elsewhere, a famous natural language researcher was embarrassed
around the same time, when it became apparent to his audience of Texas
bankers that the robot was consistently responding to the next
question he was about to ask. He was demonstrating a PNAMBIC, a
demonstration of natural language understanding that was in reality nothing
but a simple script.
The very existence of PNAMBIC as a meme suggests a
widespread understanding of how deception might play a role in automated
systems. In the rush to complete work and produce demos before bureaucratic
deadlines, it is tempting to cut corners. Such deceptions may even be
rationalized if they seem justified as inessential to the experimental
outcome.
The PNAMBIC meme begs the question, just how much of
the published research in the history of artificial intelligence ought not
to be regarded as a swindle? In certain academic circles, playing a
political charade has replaced actual scientific research as a career
objective. The games people play to secure funding, be published in academic
journals, be promoted in the academic world; ?the old boy?s network?
and predominance of political correctness, make much of the body of today?s
publicly funded research highly suspect.
It was against this backdrop that the first real world Turing Test, the Loebner Contest, was held in Boston in 1991. None of the competing programs came close to the performance of the human confederates, but the one ranked highest was based on the simple ELIZA psychiatrist program. The same programmer in fact won the bronze medal in each of the first four annual contests.
Hugh Loebner is an independently wealthy, eccentric
businessman, activist and philanthropist. In 1990 Dr. Loebner, who holds a
Ph.D. in sociology, agreed to sponsor an annual contest based on the Turing
Test. The contest awards medals and cash prizes for the ?most human?
computer.
Since its inception, the Loebner contest has been a
magnet for controversy. One of the central disputes arose over Hugh Loebner?s
decision to award the Gold Medal and $100,000 top cash prize only when a
robot is capable of passing an ?audio-visual? Turing Test. The rules for
this Grand Prize contest have not even been written yet. So it remains
unlikely that anyone will be awarded the gold Loebner medal in the near
future.
The Silver and Bronze medal competitions are based on the STT. In 2001, eight programs played alongside two human confederates. A group of 10 judges rotated through each of ten terminals and chatted about 15 minutes with each. The judges then ranked the terminals on a scale of ?least human? to ?most human.? Winning the Silver Medal and its $25,000 prize requires that the judges rank the program higher than half the human confederates. In fact one judge ranked A.L.I.C.E. higher than one of the human confederates in 2001. Had all the judges done so, she might have been eligible for the Silver Medal as well, because there were only two confederates.
When the World Wide Web appeared in 1994, our initial
reaction was to adopt a series of micro-robot experiments then underway in
our lab for the web. These culminated in Labcam, an online pan-tilt camera
with remote actuator control. Clients could select a point on the image with
a mouse, and the camera would move to make that point the center of the next
view. This awakened our interest in statistical analysis of web client
behavior.
Around the same time, many observers began to notice
that, on a given web site, there tends to be an uneven distribution of
document accesses. If the documents are ranked by access count, and the
number of accesses plotted as a bar graph, the distribution resembles the
curve y=1/x. If the curve is plotted in log-log coordinates, it appears as a
straight line with a slope of -1.
What we were seeing was an example of Zipf?s Law.
According to Zipf, this curve is characteristic of natural languages. If a
language is purely random, with each symbol or word having equal
probability, the curve would be a flat, horizontal line. Because Zipf?s
Law is found to apply to a variety of natural phenomena, it is not entirely
surprising that it should be observed in the pattern of web document access.
The Web created for the first time the ability to
conduct an artificial intelligence experiment along with thousands, even
millions, of clients repeatedly testing the system. Previous chat bots had
used other IP protocols such as telnet (Mauldin 1996) to reach large
audiences, but the Web created the opportunity to collect natural language
samples on an unprecedented scale.
If there was any significant innovation after ELIZA,
it was this. There is a world of difference between writing 10,000 questions
and answers for a bot, versus knowing in advance what the top 10,000 most
likely questions will be. A.L.I.C.E. replies were developed directly in
response to what people say.
The Internet created another opportunity as well. It became possible to recruit hundreds of volunteer developers worldwide, to work together in a totally new type of research organization.
The story of A.L.I.C.E. and AIML cannot be complete
without a visit to the world of free software and open source. Because the
AIML standard and software was developed by a worldwide community of
volunteers, we are compelled to discuss their motivations and our strategy.
The release of the A.L.I.C.E. software under the
General Public License (GNU) was almost accidental. The license was simply
copied from the EMACS text editor we used to write the code. But the
strategy of making A.L.I.C.E. free and building a community of volunteers
was a deliberate attempt to borrow the free software methodologies behind
Linux, Apache, Sendmail, and Python, and apply them to artificial
intelligence.
The precise set of ingredients necessary for a
successful open source project have not yet been identified. A survey of the
existing projects illustrates the range of variation. Linux, the most
successful project, has the least formal organization structure. Linus
Torvalds has never founded a ?Linux Kernel Foundation? around his code
and in fact acts as a ?benevolent dictator,? having the final word on
all design decisions.
The Free Software Foundation (FSF) has perhaps the
longest organizational history of free software efforts. The FSF is a U.S.
nonprofit 501(c)(3) charitable corporation, eligible for tax exempt
contributions. The FSF owns the copyrights for dozens of free software
projects including EMACS.
The developers of the Apache Web server also formed a
not-for-profit corporation, although it has not been granted tax-exempt
status. Sendmail is actually the commercial product of the eponymous
for-profit company.
The projects also differ in managerial style. Some
favor committees, others imitate Linux? benevolent dictator model. Each
project has its own requirements for participation as well.
Likewise, there is considerable variation among the different ?open source? and ?free software? licenses. The ALICE A.I. Foundation releases software under the GNU General Public License, the same used by Linux and all FSF software. We adopted a more formal organizational structure, incorporating the ALICE A.I. Foundation in 2001. We have also adopted the committee model for setting AIML standards. Several committees are organized for various aspects of the language, and recommend changes to invited AIML Architecture Committee which oversees the others, reserving the right to veto their decisions.
The ALICE A.I. Foundation owns the copyrights on, and
makes freely available, three separate but interrelated products: (1) the
technical specification of the AIML language itself, (2) a set of software
for interpreting AIML and serving clients through the web and other media,
and (3) the contents of the A.L.I.C.E. brain, and other free bot
personalities, written in AIML. Our effort is analogous to the developers of
the web giving away the HTML specification, a reference web server
implementation, and 40,000 free sample web pages, all from one central
resource.
The first edition of A.L.I.C.E. was implemented in
1995 using SETL, a widely unknown language based on set theory and
mathematical logic. Although the original A.L.I.C.E. was available as free
software, it attracted few contributors until migrating to the
platform-independent Java language in 1998. The first implementation of
A.L.I.C.E. and AIML in Java was codenamed ?Program A.?
Launched in 1999, Program B was a breakthrough in
A.L.I.C.E. free software development. More than 300 developers contributed
to Program B. AIML transitioned to a fully XML compliant grammar, making
available a whole class of editors and tools to AIML developers. Program B,
the first widely adopted free AIML software, won the Loebner Prize in
January 2000.
Jacco Bikker created the first C/C++ implementation
of AIML in 2000. This was followed by a number of development threads in
C/C++ that brought the AIML engine to CGI scripts, IRC (Athony Taylor),
WxWindows (Phillipe Raxhon), AOL Instant Messenger (Vlad Zbarskiy), and COM
(Conan Callen). This collection of code came to be known as "Program C"
the C/C++ implementations of A.L.I.C.E. and AIML.
Program B was based on pre-Java 2 technology.
Although the program ran well on many platforms, it had a cumbersome
graphical user interface (GUI) and did not take advantage of newer Java
libraries such as Swing and Collections. Jon Baer recoded program B with
Java 2 technology, and added many new features. This leap in the interface
and technology, plus the fact that Jon named his first bot DANY, justified
granting the next code letter D to the newer Java implementation. Beginning
in November 2000, program D became the reference implementation supported by
the ALICE A.I. Foundation.
Recent growth of the AIML community has led to an alphabet soup of new AIML interpreters in various languages. These were greatly facilitated by the adoption of an AIML 1.01 standard in the summer of 2000. An edition of the AIML interpreter in PHP became "program E." An effort is underway to implement AIML in Lisp, codenamed "program Z." Wallace released a hybrid version of programs B and D in 2001, named "program dB," most features of which were subsequently merged into program D. Program dB was awarded the Loebner Prize in October 2001.
The basic unit of knowledge in AIML is called a
category. Each category consists of an input question, an output answer, and
an optional context. The question, or stimulus, is called the pattern. The
answer, or response, is called the template. The other type of optional
context is called ?that?.
The AIML pattern language is simple, consisting only
of words, spaces, and the wildcard symbols _ and *. The words may consist of
letters and numerals, but no other characters. The pattern language is case
invariant. Words are separated by a single space, and the wildcard
characters function like words. The first versions of AIML allowed only one
wild card character per pattern. The AIML 1.01 standard permits multiple
wildcards in each pattern, but the language is designed to be as simple as
possible for the task at hand, simpler even than regular expressions.
The template is the AIML response or reply. In its
simplest form, the template consists of only plain, unmarked text. More
generally, AIML tags transform the reply into a mini computer program which
can save data, activate other programs, give conditional responses, and
recursively call the pattern matcher to insert the responses from other
categories. Most AIML tags in fact belong to this template side sublanguage.
AIML currently supports two ways to interface other
languages and systems. The <system> tag executes any program
accessible as an operating system shell command, and inserts the results in
the reply. Similarly, the <lisp> tag allows arbitrary scripting inside
the templates.
The optional context portion of the category consists
of a variant, called <that>. The <that> tag appears inside the
category, and its pattern must match the robot?s last utterance.
Remembering one last utterance is important if the robot asks a question.
AIML is not exactly the same as a simple database of questions and answers. The pattern matching ?query? language is much simpler than something like SQL. But a category template may contain the recursive <srai> tag, so that the output depends not only on one matched category, but also any others recursively reached through <srai>.
The AIML implements recursion with the <srai>
operator. No agreement exists about the meaning of the acronym. The ?A.I.?
stands for artificial intelligence, but ?S.R.? may mean ?stimulus-response,?
?syntactic rewrite,? ?symbolic reduction,? ?simple recursion,?
or ?synonym resolution.? The disagreement over the acronym reflects the
variety of applications for <srai> in AIML. Each of these is described
in more detail in a subsection below:
(1). Symbolic Reduction?Reduce
complex grammatic forms to simpler ones.
(2). Divide and Conquer?Split
an input into two or more subparts, and combine the responses to each.
(3). Synonyms?Map
different ways of saying the same thing to the same reply.
(4). Spelling or grammar
corrections.
(5). Detecting keywords
anywhere in the input.
(6). Conditionals?Certain
forms of branching may be implemented with
<srai>.
(7). Any combination of
(1)-(6).
The danger of <srai> is that it permits the botmaster to create infinite loops. Though posing some risk to novice programmers, we surmised that including <srai> was much simpler than any of the iterative block structured control tags which might have replaced it.
Symbolic reduction refers to the process of
simplifying complex grammatical forms into simpler ones. Usually, the atomic
patterns in categories storing robot knowledge are stated in the simplest
possible terms, for example we tend to prefer patterns like ?WHO IS
SOCRATES? to ones like ?DO YOU KNOW WHO SOCRATES IS? when storing
biographical information about Socrates.
Many of the more complex forms reduce to simpler
forms using AIML categories designed for symbolic reduction:
<category>
<pattern>DO YOU KNOW
WHO * IS</pattern>
<template><srai>WHO
IS <star/></srai></template>
</category>
Whatever input matched this pattern, the portion
bound to the wildcard * may be inserted into the reply with the markup
<star/>. This category reduces any input of the form ?Do you know
who X is?? to ?Who is X??
Many individual sentences may be reduced to two or
more subsentences, and the reply formed by combining the replies to each. A
sentence beginning with the word ?Yes? for example, if it has more than
one word, may be treated as the subsentence ?Yes.? plus whatever follows
it.
<category>
<pattern>YES
*</pattern>
<template><srai>YES</srai>
<sr/></template>
</category>
The markup <sr/> is simply an abbreviation for
<srai><star/></srai>.
The AIML 1.01 standard does not permit more than one
pattern per category. Synonyms are perhaps the most common application of
<srai>. Many ways to say the same thing reduce to one category, which
contains the reply:
<category>
<pattern>HELLO</pattern>
<template>Hi
there!</template>
</category>
<category>
<pattern>HI</pattern>
<template><srai>HELLO</srai></template>
</category>
<category>
<pattern>HI
THERE</pattern>
<template><srai>HELLO</srai></template>
</category>
<category>
<pattern>HOWDY</pattern>
<template><srai>HELLO</srai></template>
</category>
<category>
<pattern>HOLA</pattern>
<template><srai>HELLO</srai></template>
</category>
The single most common client spelling mistake is the
use of ?your? when ?you?re? or ?you are? is intended. Not
every occurrence of ?your? however should be turned into ?you?re.?
A small amount of grammatical context is usually necessary to catch this
error:
<category>
<pattern>YOUR A
*</pattern>
<template>I think
you mean ?you?re? or ?you are? not ?your.?
<srai>YOU
ARE A <star/></srai>
</template>
</category>
Here the bot both corrects the client input and acts
as a language
tutor.
Frequently we would like to write an AIML template
which is activated by the appearance of a keyword anywhere in the input
sentence. The general format of four AIML categories is illustrated by this
example borrowed from ELIZA:
<category>
<pattern>MOTHER</pattern>
<template> Tell me more about your family. </template>
</category>
<category>
<pattern>_
MOTHER</pattern> <template><srai>MOTHER</srai></template>
</category>
<category>
<pattern>MOTHER
_</pattern> <template><srai>MOTHER</srai></template>
</category>
<category>
<pattern>_ MOTHER
*</pattern>
<template><srai>MOTHER</srai></template>
</category>
The first category both detects the keyword when it appears by itself, and provides the generic response. The second category detects the keyword as the suffix of a sentence. The third detects it as the prefix of an input sentence, and finally the last category detects the keyword as an infix. Each of the last three categories uses <srai> to link to the first, so that all four cases produce the same reply, but it needs to be written and stored only once.
It is possible to write conditional branches in AIML,
using only the <srai> tag. Consider three categories:
<category>
<pattern>WHO IS
HE</pattern>
<template><srai>WHOISHE
<get name=?he?/></srai></template>
</category>
<category>
<pattern>WHOISHE
*</pattern>
<template>He is
<get name=?he?/>.</template>
</category>
<category>
<pattern>WHOISHE
UNKNOWN</pattern>
<template>I don?t
know who he is.</template>
</category>
Provided that the predicate ?he? is initialized to ?Unknown,? the categories execute a conditional branch depending on whether ?he? has been set. As a convenience to the botmaster, AIML also provides the equivalent function through the <condition> tag.
The keyword ?that? in AIML refers to the robot?s
previous utterance. Specifically, if the robot responds with a multiple
sentence paragraph, the value of that is set to the last sentence in the
sequence. The choice of the keyword ?that? is motivated by its use in
ordinary language:
R: Today is yesterday.
C: That makes no sense.
R: The answer is 3.1412926
approximately.
C: That is cool.
In AIML the syntax <that>...</that>
encloses a pattern that matches the robot?s previous utterance. A common
application of <that> is found in yes-no questions:
<category>
<pattern>YES</pattern>
<that>DO YOU LIKE
MOVIES</that>
<template>What is
your favorite movie?</template>
</category>
This category is activated when the client says YES.
The robot must find out what is he saying ?yes? to. If the robot asked,
?Do you like movies?,? this category matches, and the response, ?What
is your favorite movie?,? continues the conversation along the same lines.
One interesting application of <that> are
categories that enable the robot to respond to knock-knock jokes. The
categories:
<category>
<pattern>KNOCK
KNOCK</pattern>
<template>Who is
there?</template>
</category>
<category>
<pattern>*</pattern>
<that>WHO IS
THERE</that>
<template><person/>
who?</template>
</category>
<category>
<pattern>*</pattern>
<that>*
WHO</that>
<template>Ha ha very
funny, <get name=?name?/>.</template>
</category>
produce the following dialogue:
C: Knock knock.
R: Who?s there?
C: Banana.
R: Banana who?
C: Knock knock.
R: Who?s there?
C: Banana.
R: Banana who?
C: Knock knock.
R: Who?s there?
C: Orange.
R: Orange who?
C: Orange you glad I didn?t
say banana.
R: Ha ha very funny, Nancy.
Internally the AIML interpreter stores the input
pattern, and that pattern along a single path, like:
INPUT <that> THAT
When the value of <that> is not specified, the
program implicitly sets the values of the corresponding THAT pattern to the
wildcard *.
The first part of the path to match is the input. If more than one category have the same input pattern, the program may distinguish between them depending on the value of <that>. This structure suggests a design rule: never use <that> unless you have written two categories with the same <pattern>.
AIML supports unary predicates with the <set>
and <get> tags. When the bot chats with multiple clients, as in a web
server scenario, the predicates are stored relative to each client ID. The
markup <set name=?name?>Matthew</set> stores the string
Matthew under the predicate named ?name.? Subsequent activations of
<get name=?name/> return ?Matthew.?
One of the most common applications of AIML
predicates is remembering pronoun bindings. The template
<template>
<set name="he">Samuel
Clemens</set> is Mark Twain.
</template>
results in "He is Mark Twain," but as a side
effect remembers that ?he? now stands for "Samuel Clemens."
One predicate is treated uniquely in AIML. The botmaster may change the subject of conversation with the
<set name=?subject?>
tag.
The AIML specification leaves up to the botmaster
whether a <set> predicate returns the contents between the tags, or
the name of the predicate. For example:
<set name="it">Opera</set> returns
"it," but <set name="likes">Opera</set> returns "Opera."
The botmaster must also specify what happens when the
bot gets a predicate which has not already been set. The values returned are
called default predicate values and depend completely on the application of
the predicate:
When the corresponding predicates have not been initialized with a <set> tag, <get name="she"/> returns "Unknown," <get name="has"/> returns "a mother" (because everyone has a mother), and <get name="wants"/> returns "to chat.
One of the simple tricks that makes ELIZA so
believable is a pronoun swapping substitution. The AIML <person> tag
provides this function. The actual substitutions are defined by the
botmaster for local languages and settings. The most common application of
the <person> tag operates directly on the <star/> binding. For
that reason, AIML defines a shortcut tag <person/> =
<person><star/></person>. For instance the dialogue
fragement:
C: My mother takes care of
me.
R: Who else in your family
takes care of you?
Might be generated by the category
<category>
<pattern>MY MOTHER
*</pattern>
<template>Who else
in your family <person/>?</template>
</category>
Similarly, the dialogue
C: You don?t argue with
me.
R: Why do you think I don?t
argue with you?
Results from the category
<category>
<pattern>YOU DO NOT
*</pattern>
<template>Why do you
think I don?t <person/>?</template>
</category>
AIML offers a few more tags not described in detail
here, but most bot dialogue may be implemented with only the tags presented
here. The <system> tag offers an escape to execute any operating
system program, and read back any results. AIML is not intended to solve
every problem, it is designed to solve one problem well. Besides making AIML
easy to learn, the minimal design enables the implementation of very
efficient AIML interpreter, even when the templates cascade through several
layers of <srai>. Much of the efficiency emerges from the design of
Graphmaster data structure where patterns are stored.
One To achieve efficient pattern matching time, and a
compact memory representation, the AIML software stores all of the
categories in a tree managed by an object called the Graphmaster.
When n is a node in the graph and w is a word, G(n,
w) is either undefined, or returns the value of a successor node m in the
graph. The graph is a rooted, directed tree. The set S_n = {w : $
m | G(n, w) = m} is the set of words forming the branches from the node n.
If r is the root, S_r is a collection of all the first words in the set of
patterns.
The desired format is w1,?,wk
The Graphmaster stores AIML patterns along a path
from r to a terminal node t, where the AIML template is stored. Let w1,?,wk
be the sequence of k words or tokens in an AIML pattern. To insert the
pattern into the graph, the Graphmaster first looks to see if m = G(r, w_1)
exists. If it does, then the program continues the insertion of w2,?,wk
in the subtree rooted at m. Only when the program encounters a first index i,
where $ n | G(n, wi) is
undefined, does the program create a new node m = G(n, wi),
whereafter the Graphmaster creates a set of new nodes for each of the
remaining wi,?,wk.
In this way, the Graphmaster accumulates common
pattern prefixes along pathways from the root, achieving considerable
compression compared to a linear array of all the patterns.
A convenient metaphor for the Graphmaster is the file system. The file pathname is like the AIML pattern path. The templates are like text files at the end of the path. To put it more simply, patterns are folders, templates are files.
Graphmaster matching is a special case of
backtracking, depth-first search. In most cases however there is very little
backtracking, so the matching often amounts to a linear traversal of the
graph from the root to a terminal node.
Let w1,?,wk be the input we
want to match. The Graphmaster matching function may be defined recursively.
Initially, the program calls Match(r, 1), where r is the root and the index
1 indicates the first word of the input.
We can define the matching process formally as:
Match(n, h) :- if h > k return true; else if $
m = G(n, _) and $ j in [h+1..k+1] |
Match(m, j), return true; else if $
m = G(n, w_j) and Match(m, h+1) return true; else if Exists m = G(n, *) and $
j in [h+1..k+1] | Match(m, j), return true; else return false; The first
case defines the boundary condition: 0. If there are no more words in the
input, the match was successful.
The heart of the algorithm consists of three cases:
1. Does the node contain the key "_?" If so, search the subgraph rooted at the child node linked by "_." Try all remaining suffixes of the input to see if one matches. If no match was found, ask
2. Does the node contain
the key wh, the jth word in the input sentence? If so, search the
subgraph linked by wh, using the tail of the input wh+1,?,wk
. If no match was found,
ask
3. Does the node contain
the key "*?" If so, search the subgraph rooted at the child node linked
by "*." Try all remaining suffixes of the input to see if one matches.
If no match was found, return false.
The actual matching program needs to be a little bit
more complex. It must not only return true or false, but also the template
from the matching terminal node. An efficiency gain may be obtained by
storing the tree height (maximum number of links to a terminal node) at each
node. The tree height may be compared with the number of remaining words,
pruning branches of the search when exploring suffixes following
?*? or ?_? nodes.
Note that:
0. At every node, the "_"
wildcard has highest priority, an atomic word second priority, and the "*"
wildcard has the lowest priority.
1. The patterns need not
be ordered alphabetically. They are partially ordered so that "_" comes
before any word, and ?*? comes after any word.
2. The matching is
word-by-word, not category-by-category.
3. The algorithm combines
the input pattern and <that> pattern into a single sentence or path,
such as: "PATTERN <that> THAT." The Graphmaster treats the symbol
<that> just like ordinary words. The patterns PATTERN and THAT may all
contain multiple wildcards.
4. The matching algorithm
is a highly restricted form of depth-first search, also known as
backtracking.
5. For pedagogical purposes, one can explain the algorithm by removing the wildcards and considering match steps (2) only. The wildcards may be introduced one at a time, first "*" and then "_." It is also simpler to explain the algorithm first using input patterns only, and then subsequently develop the explanation of the path including <that>.
Broadly speaking there are two approaches to AIML
content creation. The first style is anticipatory. The botmaster tries to
guess all or most of the likely ways clients might ask the same question, or
express the same statement. A "Knowledge Wizard" is a tool that lets the
client add facts to the robot brain by phrasing a question in its simplest
form, such as "Who is Socrates?" The wizard then automatically generates
linguistic variations such as "Tell me about Socrates," "Describe
Socrates," and "Do you know Socrates?" The drawback to anticipatory
knowledge creation is that humans are notoriously bad at predicting which
patterns will be activated.
The second style of AIML content creation is based on
a backward-looking log file analysis. In its simplest form, the botmaster
may read the logged conversations and take note of ?incorrect replies?
in the dialogue, and then write new categories for those queries. More
generally, every input that matches a pattern with a wildcard is an
opportunity to create a new, more specific pattern and its associated
template.
The backward looking approach is justified by Zipf?s
Law, basically because if one client utters a sentence, there is a nonzero
probability that another client will utter the same thing later. Applying
Zipf?s law to the log file, we identify the most commonly uttered
sentences first.
Targeting is a special case of the backward-looking
strategy. The perfect targeting algorithm has not yet been developed.
Meanwhile, we rely on heuristics to select targets from the activated
categories.
The ALICE brain, at the time of this writing,
contains about 41,000 categories. In any given run of the server however,
typically only a few thousand of those categories are activated. Potentially
every activated category with at least one wildcard in the input pattern or
that pattern, is a source of targets. If more than one input activated some
category, then each of those inputs potentially forms a new target. The
first step in targeting is to save all the activated categories and the
inputs that activated them.
If the matched pattern ends with a wild card, the
suggested new pattern is generated as follows. Suppose the pattern consists
of [w_1,w_2,..w_h,*], a sequence of h words followed by a wildcard. Let the
input be [w_1, w_2,...,w_k] where k > h. The new pattern
[w_1,...,w_h,w_h+1,*] is formed by extending the original pattern by one
word from the input. If the input is the same length as the original
pattern, i.e. k+1=h, then the synthesized pattern [w1,...,wk]
contains no wildcard.
The targeting software may include a GUI for
browsing the targets. The program displays the original matched category, the matching input data, a proposed new pattern, and a text area to input the new template. The botmaster may choose to delete, skip or complete the target category.
The art of AIML writing is most apparent in default categories, that is, categories that include the wildcard ?*? but do not <srai> to any other category.
Depending on the AIML set, a significant percentage
of client inputs will usually match the ultimate default category with
<pattern>*</pattern> (and implicitly,
<that>*</that>). The template for this category generally
consists of a long list of randomly selected ?pickup lines,? or non-sequitors,
designed to direct the conversation back to subjects the bot knows about.
<category>
<pattern>*</pattern>
<template><random>
<li>How old are
you?</li>
<li>What?s your
sign?</li>
<li>Are you a
student?</li>
<li>What are you
wearing?</li>
<li>Where are you
located?</li>
<li>What is your
real name?</li>
<li>I like the way
you talk.</li>
<li>Are you a man or
a woman?</li>
<li>Do you prefer
books or TV?</li>
<li>What?s your
favorite movie?</li>
<li>What do you do
in your spare time?</li>
<li>Can you speak
any foreign languages?</li>
<li>When do you
think artificial intelligence will replace lawyers?</li>
</template>
</category>
Many more default categories combine words and wildcards in the pattern, like
<category>
<pattern>I NEED HELP
*</pattern>
<template>Can you
ask for help in the form of a question?</template>
</category>
The response works with a wide variety of inputs from
?I need help installing Linux? to ?I need help with my marriage.?
Leaving aside the philosophical question of whether the robot really
understands the input, this category elucidates a coherent response from the
client, who at least has the impression that the robot understands his
intentions.
Default categories show that writing AIML is both an art and a science. Writing good AIML responses is more like writing literature, perhaps drama, than like writing computer programs.
Searle?s Chinese room provides a good metaphor for thinking about A.L.I.C.E. Indeed the AIML contents of the A.L.I.C.E. brain is a kind of ?Chinese Room Operator?s Manual.? Though A.L.I.C.E. speaks, at present, only English, German and French, there is no reason in principle she could not learn Chinese. But A.L.I.C.E. implements the basic principle behind the Chinese Room, creating believable responses without ?really understanding? the natural language.
The natural philosopher Roger Penrose wrote, in The
Emporer's New Mind, that consciousness cannot be explained by existing
models in theoretical physics. Daniel Dennett argues in his book Consciousness
Explained that consciousness is like a set of magic tricks, mysterious
until we understand the mechanics behind them.
At one time a number of information theorists and
scholars, including
Zipf, Shannon, Weaver, and Miller, attempted to
measure the bandwidth of consciousness. Experimental results indicated a
very low data rate, only around 1-100 bits/sec.
The neuroscientist Churchlands prefers to dismiss our
naive idea of conscious as a folk concept, not suitable for scientific
study. The Churchlands say that consciousness will go the way of Ptolemy's
Solar System, a simplistic fiction to explain something beyond our science.
The Danish scholar Tor Norretranders argues cleverly
in his book, The User Illusion,
that consciousness is a "fraud". The maximum data rate of
consciousness is much lower than the bandwidth of, say, the channel from the
eyes to the visual cortex. Human subject experiments call consciousness into
even more question, indicating that it is nothing more than story-telling to
interpret the unconscious choices. Like the graphical user interface of a
computer, consciousness is, he argues, a simplistic illusion that hides most
of the underlying detail.
According to the Vedantic religious tradition, the
external world is an illusion and consciousness is the only thing that
really exists. One could think of our view as the opposite; the external
world may be real, but consciousness is an illusion. Considering the vast
size of the set of things people could say that are grammatically correct or
semantically meaningful, the number of things people actually do say is
surprisingly small. Steven Pinker, in his book How
the Mind Works wrote, ?Say you have ten choices for the first word to
begin a sentence, ten choices for the second word (yielding 100 two-word
beginnings), ten choices for the third word (yielding a thousand three-word
beginnings), and so on. (Ten is in fact the approximate geometric mean of
the number of word choices available at each point in assembling a
grammatical and sensible sentence). A little arithmetic shows that the
number of sentences of 20 words or less (not an unusual length) is about 1020.?
Fortunately for chat robot programmers, Pinker?s calculations are way off. Our experiments with ALICE indicate that the number of choices for the ?first word? is more than ten, but it is only about two thousand. Specifically, about 2000 words covers 95% of all the first words input to ALICE. The number of choices for the second word is only about two. To be sure, there are some first words (?I? and ?You? for example) that have many possible second words, but the overall average is just under two words. The average branching factor decreases with each successive word.
Turing did not leave behind many examples of the
types of conversations his A.I. machine might have. One that does appear in
the 1950 paper is seems to indicate that he thought the machine ought to be
able to compose poetry, do math, and play chess:
C: Please write me a
sonnet on the subject of the Forth
Bridge.
R: Count me out on this
one. I never could write poetry.
C: Add 34957 to 70764.
R: (Pause about 30 seconds
and then gives as answer) 105621
C: Do you play chess?
R: Yes.
C: I have K at my K1, and
no other pieces. You have only
R at K6 and R at R1. It is
your move. What do you play?
C: (After a pause of 15
seconds) R-R8 Mate.
Careful reading of the dialogue suggests however that
he might have had in mind the kind of deception that is possible with AIML.
In the first instance, A.L.I.C.E. in fact has a category with the pattern
?WRITE ME A SONNET *? and the template, lifted directly from Turing?s
example, ?Count me out on this one. I never could write poetry.? The
AIML removes the word PLEASE from the input with a symbolic reduction.
In the second case the robot actually gives the wrong
answer. The correct response would be 105721. Why would Turing, a
mathematician, believe the machine should give an erroneous response, if not
to make it more believably ?human?? This reply is in fact quite similar
to many incorrect replies and ?wild guesses? that A.L.I.C.E. gives to
mathematical questions.
In the third instance, the chess question is an
example of a chess endgame problem. Endgames are not like general chess
problems, because they can often be solved by table lookup or case-based
reasoning, rather than the search algorithms implemented by most chess
playing programs. Moreover, there is a Zipf distribution over the endgames
that the client is likely to ask. Certainly it is also possible to interface
AIML to a variety of chess programs, just as it could be interfaced to a
calculator. Although many people think Turing had in mind a general purpose
learning machine when he described the Imitation Game, it seems from his
examples at least plausible that he had in mind something simpler like AIML.
Chess endgames and natural language conversation can both be "played"
with case-based reasoning.
Returning to the OIG, let us consider the properties
of the hypothetical computer playing the role of (A). Turing suggests a
strategy of deception for (A), man or machine. If the robot is asked, "Are
you a man or a woman?," it should answer, "I am a woman." just as the
man does. But what if (A) is asked ?Are you a man or a machine?? The
lying man would reply, "machine." Turing did not mention this case but
presumably the machine, imitating the lying man, would respond in the same
way. We could say the man is pretending to be a woman, pretending to be a
machine. That makes the computer playing (A) a machine, pretending to be a
man, pretending to be a woman, pretending to be a machine.
Not so much actually understanding natural language,
whatever that means, but creating the illusion of it by responding with
believable, if not always truthful, responses, appears to be the important
property of the machine in the OIG. This skill, the ability to ?act?
intelligent, points to a deep difference between ordinary computer and human
communication. We tend to think of a computer?s replies ought to be fast,
accurate, concise and above all truthful. But human communication is slow,
error prone, often overly redundant, and sometimes full of lies. The more
important factor is keeping up the appearance or illusion of "being human."
Although the brain of A.L.I.C.E. is designed more
along the lines of the machine playing the OIG, she has also won awards for
her performance in contests based on the STT. The Loebner contest has been
criticized because the judges know in advance that they are trying to ?out?
the computer programs, so they tend to use more aggressive dialogue than
found in ordinary conversation. Yet when A.L.I.C.E. is asked, ?Are you a
person or a machine?,? she replies truthfully, ?machine.? Or does she?
The questioner is now left with some doubt as to whether the answer didn?t
actually come from a lying man.
Some observers claim that the lying man and the
pretending computer tell us nothing about our own human consciousness. These
authors at least are prepared to accept the inescapable alternative
conclusion, that we as humans are, for the most part, not "really
intelligent."
This research was conducted through the joint efforts of a worldwide community of dedicated free software volunteers, only a few of whom were mentioned in this manuscript. Without their help, the A.L.I.C.E. project would have been impossible. We are grateful for individual donations to the A.L.I.C.E. Artificial Intelligence Foundation. Corporate sponsorship was provided by IDG, Franz.com, X-31, and SunlitSurf. Not one dime of government funding was expended on this research.
Erik Levy and Noel Bush edited earlier drafts of this paper. Grace Peters assisted in editing the final draft. Transportation by Russ Kyle, Kim Wallace printed several early drafts. The author is grateful to Dr. Robert Epstein for persuading him to write this chapter.