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                            INTRODUCING MEGAHAL

Jason L. Hutchens                                       Michael D. Alder
Dept. of E&E Engineering                            Dept. of Mathematics
University of Western Australia          University of Western Australia
Nedlands W.A. 6907, Australia              Nedlands W.A. 6907, Australia
hutch@ciips.ee.uwa.edu.au                          mike@maths.uwa.edu.au



Abstract

Conversation simulators are computer programs which give the appearance of
conversing with a user in natural language.  Alan Turing devised a simple
test in order to decide whether such programs are intelligent.  In 1991,
the Cambridge Centre for Behavioural Studies held the first formal
instantiation of the Turing Test.  In this incarnation the test was known as
the Loebner contest, as Dr. Hugh Loebner pledged a $100,000 grand prize for
the first computer program to pass the test.  In this paper we give a brief
background to the contest, before describing in detail the workings of
MegaHAL, the primary author's entry to the 1998 Loebner contest.



1. Introduction

Alan Turing was a brilliant British mathematician who played a great role in
the development of the computer.  The imitation game, nowadays known as the
Turing test, was devised by Turing as a method for deciding whether or not a
computer program is intelligent.

The Turing test takes place between an interrogator and two subjects.  The
interrogator communicates with these subjects via a computer terminal, and
must decide which is a human being and which is a computer program.  The
human being helps the interrogator to make the correct identification, while
the computer program attempts to trick the interrogator into making the
wrong identification.  If the latter case occurs, the computer program is
said to be exhibiting intelligence (Turing, 1992).

One of the great advantages of the Turing test is that it allows the
interrogator to evaluate almost all of the evidence that we would assume to
constitute thinking (Moor, 1976).  For instance, the interrogator can pose
hypothetical situations in order to ask the subjects how they would react.

Alan Turing died in 1954, a decade before conversation simulators such as
ELIZA emerged.  It is indeed unfortunate that he did not live to witness
his test being performed.  One cannot help but think that he would have
been disappointed.



2. The Loebner Contest

Apart from a few limited tests performed by programmers of conversation
simulators (Colby, 1981), the Turing test was not formally conducted until
1995.  Although the inaugural Loebner contest, held in 1991, was touted as
the first formal instantiation of the Turing test, it was not until 1995
that it truly satisfied Turing's original specifications (Hutchens, 1996).

The first Loebner contest was held on the 8th of November 1991 in Boston's
Computer Museum.  Because this was a contest rather than an experiment, six
computer programs were accepted as subjects.  Four human subjects and ten
judges were selected from respondents to a newspaper advertisement; none of
them had any special expertise in Computer Science (Epstein, 1992).

The original Turing test involved a binary decision between two subjects by
a single judge.  With ten subjects and ten judges, the situation was
somewhat more complex.  After months of deliberation, the prize committee
developed a suitable scoring mechanism.  Each judge was required to rank
the subjects from least human-like to most human-like, and to mark the
point at which they believed the subjects switched from computer programs
to human beings.

If the median rank of a computer program exceeded the median rank of at
least one of the human subjects, then that computer program would win the
grand prize of $100,000[1].  If there was no grand prize winner, the
computer program with the highest median rank would win the contest with a
prize of $2000.



3. Conversation Simulators

Since its inception, the Loebner contest has primarily attracted hobbyist
entries which simulate conversation using template matching; a method
employed by Joseph Weizenbaum in his ELIZA conversation simulator, developed
at MIT between 1964 and 1966.  Put simply, these programs look for certain
patterns of words in the user's input, and reply with a pre-determined
output, which may contain blanks to be filled in with details such as the
user's name.

Such programs are effective because they exploit the fact that human beings
tend to read much more meaning into what is said than is actually there; we
are fooled into reading structure into chaos, and we interpret non-sequitur
as whimsical conversation (Shieber, 1994).

Weizenbaum was shocked at the reaction to ELIZA.  He noticed three main
phenomenon which disturbed him greatly (Weizenbaum, 1976):

1. A number of practising psychiatrists believed that ELIZA could grow into
   an almost completely automatic form of psychotherapy.

2. Users very quickly became emotionally involved---Weizenbaum's secretary
   demanded to be left alone with the program, for example.

3. Some people believed that the program demonstrated a general solution to
   the problem of computer understanding of natural language.

Over three decades have passed since ELIZA was created.  Computers have
become significantly more powerful, while storage space and memory size
have increased exponentially.  Yet, at least as far as the entrants of the
Loebner contest go, the capabilities of conversation simulators have
remained exactly where they were thirty years ago.  Indeed, judges in the
1991 contest said that they felt let down after talking to the computer
entrants, as they had had their expectations raised when using ELIZA during
the selection process.



4. MegaHAL

In 1996 the primary author entered the Loebner contest with an ELIZA
variant named HeX, which was written during his spare time in under a
month.  Apart from the lure of the prize money, a major motivation for the
entry was a desire to illustrate the shortcomings of the contest (Hutchens,
1996).  A considerably more powerful program, SEPO, was entered the
following year, where it was placed second.  We believe this to be
indicative of a gradual improvement in the quality of the contestants.

The program submitted to this year's contest, MegaHAL, uses a significantly
different method of simulating conversation than either HeX or SEPO, and we
dedicate the remainder of this paper to describing its workings.

4.1 Markov Modelling

MegaHAL is able to construct a model of language based on the evidence it
encounters while conversing with the user.  To begin with, the input
received from the user is parsed into an alternating sequence of words and
non-words, where a word is a series of alphanumeric characters and a
non-word is a series of other characters.  This is done to ensure not only
that new words are learned, but that the separators between them are
learned as well.  If the user has a habit of putting a double space after a
full stop, for instance, MegaHAL will do just the same.

The resulting string of symbols[2] is used to train two 4th-order Markov
models (Jelinek, 1986).  One of these models can predict which symbol will
following any sequence of four symbols, while the other can predict which
symbol will precede any such sequence.  Markov models express their
predictions as a probability distribution over all known symbols, and are
therefore capable of choosing likely words over unlikely ones.  Models of
order 4 were chosen to ensure that the prediction is based on two words;
this has been found necessary to produce output resembling natural language
(Hutchens, 1994).
4.2 Generating Candidate Replies

Using a Markov model to generate replies is easy; Shannon was doing much
the same thing by flipping through books back in 1949 (Shannon, 1949).
However, such replies will often be nonsensical, and will bear no
relationship to the user's input.

MegaHAL therefore attempts to generate suitable replies by basing them on
one or more keywords from the user's input.  This explains why two Markov
models are necessary; the first model generates a sentence from the keyword
on, while the second model generates the remainder of the sentence, from
the keyword back to the beginning.

Keywords are obtained from the users input.  Frequently occurring words,
such as "the", "and" and "what", are discarded, as their presence in the
input does not mean they need to be present in the output.  The remaining
words are transformed if necessary---"my" becomes "your" and "why" becomes
"because", for example.  What remains is used to seed the output.

4.3 Selecting a Reply

MegaHAL is able to generate many hundreds of candidate replies per second,
each of which contain at least one keyword.  Once a small time period has
elapsed, the program must display a reply to the user.  A method is needed
for selecting a suitable reply out of the hundreds of candidates.

   I(w|s) = -log P(w|s)       - Equation 1

MegaHAL chooses the reply which assigns the keywords the highest
information.  The information of a word is defined in Equation 1 as the
surprise it causes the Markov model.  Hence the most surprising reply is
selected, which helps to guarantee its originality.  Note that P(w|s) is
the probability of word w following the symbol sequence s, according to the
Markov model.

The algorithm for MegaHAL proceeds as follows:

* Read the user's input, and segment it into an alternating sequence of
  words and non-words.

* From this sequence, find an array of keywords and use it to generate
  many candidate replies.

* Display the reply with the highest information to the user.

* Use the user's input to update the Markov models, so that MegaHAL can
  learn from what the user types.

This sequence of steps is repeated indefinitely, which allows the program
to learn new words, and sequences of words, as it converses with the user.

4.4 Training MegaHAL

When MegaHAL is started it has no knowledge of language, and is unable to
give a reply at all---the program needs to be trained using a source of
text to ensure that it does not reveal its identity prematurely.  A large
corpus of training data was created for this purpose.

The training data is made up of various texts:

* Hand-crafted sentences designed in order to create a personality for
  MegaHAL, including sentences containing a false name, age and occupation.

* Encyclopaedic information taken from the Web, on topics such as
  geography, music, sports, movies and history.

* A selection of sentences picked from transcripts of previous Loebner
  contests.

* Lines of dialogue taken from scripts for movies and television shows.

* Lists of popular quotations.

* A small amount of text in languages other than English.

When MegaHAL is trained using this data, it is able to respond to questions
on a variety of topics.  It is hoped that the program will also learn new
topics from the judges, although this remains to be seen.

4.5 Online Experimentation

MegaHAL has been available on the Web since early in 1997, and hundreds of
users converse with it every day.  It is an interesting fact that one never
tires of reading transcripts of conversation, due to MegaHAL's ability to
respond with original replies.

Many users are often offended by the things MegaHAL says, and some believe
that they have been personally insulted.  One user, named Forrest, was
quite incensed when the program began quoting parts of the Forrest Gump
screenplay back at him.  That a computer program can cause such an
emotional response in a human being is interesting, although it may say
more about the human being than it does about the program.

Users are often impressed with MegaHAL's ability to learn.  One user was
annoyed that the program had learned more about his personal life than he
would care it to know, while another stated that MegaHAL would eventually
grow into a person of average intellect (he attributed this bold claim to
the law of averages).  One user, experienced working with people in
psychotic crises, likened talking to MegaHAL with talking to a psychotic.

Users have succesfully taught the program to respond to sentences in
French, Spanish, Greek, German, Italian, Latin, Jananese and Hebrew,
amongst others.  A clergyman spent hours teaching MegaHAL about the love
of Jesus, only to constantly receive blasphemous responses.

The reaction of Web user's to the program has been surprising, and is
pretty much what Weizenbaum experiences with ELIZA.  MegaHAL generates
gibberish mostly, but occasionally, by pure coincidence, it will come out
with an appropriate response.  It is these occasions that stick in the
mind, and give cause for over-zealous claims of computational intelligence.

4.6 Example Interaction

As an example of MegaHAL at its best, we give here a few extracts from
a conversation which took place, over a period of three hours in mid 1997,
between MegaHAL and an anonymous Web user.

To begin with, the user was able to successfully teach the program
some basic facts:

   User:    Roentgen discovered the X-rays.
   MegaHAL: THE NOBEL PRIZE FOR PHYSICS IN 1900 WAS WON BY ROENTGEN.
   User:    Who discovered the X-rays?
   MegaHAL: ROENTGEN DISCOVERED THE X-RAYS.

He then discovered that the program is an expert an being nonsensical.
Even so, MegaHAL was still able to give some appropriate responses, due to
the keyword mechanism for generating replies:

   MegaHAL: WHEN WE BEGIN TO RESOLVE SOME OF MY THUMBS, SOMETHING
            WICKED THIS WAY COMES
   User:    what is the meaning of your last assertion ?
   MegaHAL: HAVE YOU LEARNED SINCE WE LAST SPOKE?
   User:    I've learned I'm not experienced enough to modelize your
            brain system
   MegaHAL: YOU'VE FINALLY CONFUSED ME.
   User:    I've learned nothing
   MegaHAL: HAVE YOU LEARNED ANYTHING ABOUT INSANITY?
   User:    are you stupid
   MegaHAL: I AM NOT STUPID! REALLY!

Finally, the user discovered that the program could respond in other
languages:

   User:    Tu parles francais?
   MegaHAL: I GUESS SO. PARLEZ-VOUS FRANCAIS?
   User:    L'amour est une passion humaine.
   MegaHAL: LA LANGIDOCE EST UNE PETITE JEUNNE FILLE

In general MegaHAL's conversations are not as successful as this.  Most
users are satisfied with typing in rude words to see how the program
responds.



5. Conclusion

MegaHAL uses a technique which differs significantly from that used by
previous entrants to the Loebner contest.  It has been submitted in 1998
for the purpose of demonstrating a different method of simulating
conversation.  Although its replies are occasionally lucid, MegaHAL is most
definitely not an Artificial Intelligence; we must be careful not to read
too much into what it says.

The Loebner contest does offer some benefits (Loebner, 1994); it provides
an annual Turing test for anyone who cares to submit an entry, it promotes
and stimulates interest in the field of Artificial Intelligence, it
encourages competition, it could conceivably result in new techniques which
may be applicable to fields outside of Artificial Intelligence and it
stimulates discussion amongst researchers.  Even so, we believe that the
contest is not advancing the field of Artificial Intelligence because,
although the $2000 is a guaranteed reward, it is not a large enough carrot
to entice serious research groups.

Perhaps the most important contribution of the Loebner contest is the
insight it provides into the psychology of conversation---it makes us aware
of how little our understanding of conversation lies in what is said.



Notes

1. Today the program must also satisfy audio-visual requirements to win the
   grand prize.

2. A symbol refers to both words and non-words.



References

Colby, Kenneth Mark.  1981.  Modeling a paranoid mind.  The Behavioral and
Brain Sciences, 4:515--560.

Epstein, Robert.  1992.  Can machines think?  AI Magazine, Summer:80--95.

Hutchens, Jason L.  1994.  Natural language grammatical inference.
Honour's thesis, University of Western Australia, December 1994.  Available
at: http://ciips.ee.uwa.edu.au/Papers/

Hutchens, Jason L.  1996.  How to pass the turing test by cheating.
Available at: http://ciips.ee.uwa.edu.au/Papers/

Jelinek, Frederick.  1986.  Markov source modeling of text generation.
Technical report, IBM T.J. Watson Research Center.

Loebner, Hugh.  1994.  In response to lessons from a restricted Turing
test.  Available at: http://acm.org/~loebner/In-response.html

Moor, James H.  1976.  An analysis of the turing test.  Philosophical
Studies, 30:249--257.

Shannon, Claude E. and Warren Weaver.  1949.  The Mathematical theory of
Communication.  University of Illinois Press.

Shieber, Stuart M.  1994.  Lessons from a restricted turing test.
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Turing, A.M.  1992.  Computing machinery and intelligence.  In D.C. Ince,
editor, Collected works of A.M. Turing: Mechanical Intelligence. Elsevier
Science Publishers, chapter 5, pages 133--160.

Weizenbaum, Joseph.  1976.  Computer Power and Human Reason.  W.H. Freeman
and Company.