Selmer Bringsjord specializes in the logico-mathematical and
philosophical foundations of Artificial Intelligence and Cognitive Science.
He received a bachelor's degree from the University of Pennsylvania,
and a PhD from Brown University.
Since 87 he has been on faculty in the Departments of Philosophy, Psychology
and Cognitive Science, and Computer Science, at Rensselaer Polytechnic
Institute (RPI) in Troy, New York.
Bringsjord was on Rensselaer's team that won the prestigious Hesburgh
Award (1995) for excellence in undergraduate education (for technology-based
intereactive learning). He was also a Lilly Fellow in 1989. He is co-director
of the Autopoesis Project (in AI and creativity).
Bringsjord is author of the critically acclaimed What Robots Can &
Can't Be (1992, Kluwer; ISBN 0-7923-1662-2), which is concerned with the
future of attempts to create robots that behave as humans. Two new technical
books, Superminds: A Defense of Uncomputable Cognition, and Artificial
Intelligence and Literary Creativity: Inside the Mind of Brutus, A Storytelling
Machine, are out this year (Kluwer Academic/Lawrence Erlbaum). His book
Abortion: A Dialogue has been published this past Fall by Hackett.
He has lectured and interviewed in person across the United States,
Is It Possible to Build Dramatically
Compelling Interactive Digital Entertainment
(in the form, e.g., of computer games)?*1
by Selmer Bringsjord
Rensselaer Polytechnic Institute (RPI)
Lots of computer games are compelling. E.g., I find even current computerized
poker games quite compelling, and I find The Sims downright fascinating;
doubtless you have your own favorites. But our planet isn't graced by
even one dramatically compelling computer game (or, more generally,
one such interactive digital entertainment). The movie T2, Dante's
Inferno, Hamlet, Gibson's prophetic Neuromancer,
the plays of Ibsen -- these things are dramatically compelling: they succeed
in no small part because they offer captivating narrative, and all that
that entails (e.g., engaging characters). There is no analogue in the
interactive digital arena, alas. Massively multi-player online games are
digital, interactive, and entertaining -- but they have zero literary
power (which explains why, though T2 engages young kids through
at least middle-aged professors, such games are demographically one-dimensional).
The same can be said, by my lights, for all other electronic genres.
This state of affairs won't change unless a number of key challenges
are conquered; and conquering them will require some seminal advances
in the intersection of artificial intelligence (AI) and narrative. (E.g.,
since interactive digital narrative will need to be crafted and massaged
as the story is unfolding, computers, not slow-by-comparison
humans, will need to be enlisted as at least decent dramatists -- but
getting a computer to be a dramatist requires remarkable AI.) In this
paper, I discuss one of these challenges for the start of the new millennium:
the problem of building dramatically compelling virtual characters.
Within this challenge I focus upon one property such characters presumably
must have: viz., autonomy.
We have dramatically compelling non-interactive digital (= electronic)
entertainment: sit down and watch The Matrix (see Figure 1) or T2 (see Figure
2). We have compelling interactive
digital entertainment; my favorites include console-based sports games.2 We have dramatically compelling
interactive entertainment; for example, improvizational theatre. What
we don't have is dramatically compelling interactive digital
entertainment. People sometimes tell me that there is a counter-example
to my negative view to be found in online multi-player games, but though
such games are digital, interactive, and entertaining -- they have zero
literary power (which explains why, though T2 engages young kids
through at least middle-aged professors, such games are demographically
one-dimensional). The same can be said, by my lights, for all other electronic
genres. Can we build systems that imply and affirmative answer to the
title of this paper? My answer is: ``Maybe, maybe not; but at any rate
I can tell you, at least in broad strokes, what some of the hurdles are,
from the standpoint of AI. And I can tell you, today, about one specific
Figure 1: From a Dramatically Compelling Scene
in The Matrix
Before we go any further, let's make sure we're realistic about the driving
question, and thereby start with a provisional answer of ``I don't know."
Such realism will buck the trend. For unfortunately, realistic positions
on the advance of AI are rather hard to come by. There are two reasons
The first reason is that lots of people are either ignorant of or tendentiously
choose to ignore the underlying mathematical facts. These facts include
that some problems can be solved by computing machines, and others can't,
and that most can't. So whenever you ask whether a problem P can
be solved by a computing machine, where P is such that it isn't
known that there is an algorithm for solving it, honesty should imply
Figure 2: POV of the Terminator in T2
The second reason why realism in the face of questions like that which
drives the present investigation is in short supply is that we have lots
of silly prophets. For example, in the June 19, 2000 issue of TIME
magazine, devoted to ``The Future of Technology," we hear from author
and inventor Ray Kurzweil that nanobots (microscopic robots) will by 2030
be able to map out a synthetic duplicate of your brain after you swallow
(yes, swallow) a few of them. This duplicate will be instantiated in computer
hardware 10 million times faster than the sluggish, old-fashioned grey
stuff inside your cranium; the result will be an artificial intelligence
immeasurably more clever than you. Vernor Vinge, associate professor of
mathematics and computer science at San Diego State University, is another
example. Prophesying for the Chronicle of Higher Education (July
12, 2000; online edition), he gives us a more compressed timeline: by
his lights, on the strength of the trend that the speed of computer hardware
doubles every 18 months, computers will be more intelligent than all humans
at some point within 20 years. This point he calls ``The Singularity,"
which ushers in post-humanity, an age in which humans are left in the
dust by machines that get exponentially smarter by the day (if not the
nanosecond). For a third example, consider Hans Moravec, who in his latest
book, Robot: Mere Machine to Transcendent Mind, informs us that
because hardware is getting faster at the rate Vinge cites, by 2040 ``fourth
generation" robots will exceed humans in all respects, from running companies
to writing novels. Such robots will evolve to such lofty cognitive heights
that we will stand to them as single-cell organisms stand to us today.
Many others in the field of Artificial Intelligence (AI) predict the same
sensational future unfolding on about the same rapid schedule.
The gaming industry will not be this lucky; I'm sure of it. Today I'll
tell you, briefly, why.
Let make explicit one presupposition before we begin in earnest. I assume
that AI in general, along with the AI part of the gaming industry in particular,
have realized that non-logicist AI isn't magic. What do I mean? I mean
that people should be smart enough now to concede that no system is going
to automatically learn, through subsymbolic, numerical processing, how
to, say (and this is the challenge I'm going to focus on below), assemble
robust, autonomous characters in a game. Automated learning through artificial
neural networks and genetic algorithms are fine for certain applications,
but gone, I assume, are the days of wild optimism about the ability of
such learning techniques to automatically yield (to stick with this example)
full-blooded NPCs.4 We really only have two overall
approaches to AI: one based on logic, and one based on subsymbolic processing.
This paper is written from the perspective of logic. If you think that
you have a way of solving the problems I'm talking about without using
logic, I wish you well.
Very well. So, why is it that building dramatically compelling interactive
digital entertainment (in the form, e.g., of games) is so difficult? There
are many reasons, among which fall the following.
- C1: Formalizing Literary Themes.
If Dave Ferrucci and I are right, plotlines and so-called 3-dimensional
characters aren't enough for a computer to generate first-rate narrative:
you also need to instantiate immemorial themes -- betrayal (the one
we focus on in (Bringsjord & Ferrucci, 2000)), self-deception
(a theme we employ in BRUTUS), unrequited love, revenge,
and so on. If such themes are to be used by story-managing machines,
they must be represented; if they are to be represented and exploited,
they need to be rigorously represented and reasoned over.
Such rigorous representation and reasoning is very hard to come by.
- C2: Story Mastery.
After interactive drama begins, things can devolve toward the uninteresting.
If ``hack-and-slash" is all that is sought from an interactive game,
then such devolution may be acceptable. But if genuine drama is desired,
then something or someone must ensure that what happens is dramatically
interesting. One possibility (with respect to a multi-player online
game) is to have a human or humans oversee the action as it unfolds,
and make changes that keep things ``on track." For obvious reasons,
in a rapidly progressing game with thousands of human players, this
is impracticable; the possibility of human oversight is purely conceptual.
So we must turn to computers to automate the process. But how? How
is the automation to work? I've assumed that if a program could be
built that writes compelling fiction, we might thereby have taken
significant steps toward a program that can serve as a story master.
- C3: Building Robust, Autonomous Characters.
A sine qua non for compelling literature and drama is the
presence of robust, autonomous, and doxastically sophisticated5 characters. In short, such literature
and drama exploits the central properties of being a person. (In many
cases, great stories come to be remembered in terms of great characters.)
This presents a problem for interactive electronic entertainment:
how do we build an electronic character that has those attributes
that are central to personhood, and whose interaction with those humans
who enter the virtual worlds is thereby compelling?
- C4: Personalization.
If virtual characters are going to react intelligently to you as
user or gamer, they must, in some sense, understand you.
This problem is directly related to C3, because the characters must
have sophisticated beliefs about you and your beliefs, etc.
In the remainder of this paper, I focus on C3, and moreover I focus within
this challenge on the specific problem of building autonomous characters.
The plan is as follows. I begin by reviewing the concept of an intelligent
agent in AI. I then explain the clash between this limited concept
and the kind of properties that are distinctive of personhood; one of
these properties is autonomy, or ``free will." In order to highlight the
problem of imparting autonomy to a virtual character, I turn to what I
have dubbed ``The Lovelace Test." I conclude with a disturbing argument
that seems to show that virtual characters, as intelligent agents, can't
be autonomous, because they would inevitably fail this test. I do intimate
my own reaction to this argument.
As the century turns, all of AI has been to an astonishing degree unified
around the conception of an intelligent agent. The unification has in
large part come courtesy of a comprehensive textbook intended to cover
literally all of AI: Russell and Norvig's (1994) Artificial Intelligence: A Modern
Approach (AIMA), the cover of which also displays the phrase
``The Intelligent Agent Book." The overall, informal architecture for
an intelligent agent is shown in Figure 3; this is taken directly from the
AIMA text. According to this architecture, agents take percepts
from the environment, process them in some way that prescribes actions,
perform these actions, take in new percepts, and continue in the cycle.6
Figure 3: The Architecture of an Intelligent
In AIMA, intelligent agents fall on a spectrum from least intelligent
to more intelligent to most intelligent. The least intelligent artificial
agent is a ``TABLE-DRIVEN-AGENT,"
the program (in pseudo-code) for which is shown in Figure 4. Suppose that we have a set of actions
each one of which is the utterance of a color name (``Green," ``Red,"
etc.); and suppose that percepts are digital expressions of the color
of an object taken in by the sensor of a table-driven agent. Then given
Table 1 our simple intelligent agent, running the program
in Figure 4, will utter (through a voice synthesizer,
assume) ``Blue" if its sensor detects 100. Of course, this is a stunningly
dim agent. What are smarter ones like?
Figure 4: The Least Intelligent Artificial
Table 1: Lookup Table for TABLE-DRIVEN-AGENT
Figure 5: Program for a Generic Knowledge-Based
In AIMA we reach artificial agents that might strike some as
rather smart when we reach the level of a ``knowledge-based" agent. The
program for such an agent is shown in Figure 5. This program presupposes an agent that
has a knowledge-base (KB) in which what the agent knows is stored
in formulae in the propositional calculus, and the functions
- TELL, which injects sentences (representing facts)
which generates a propositional calculus sentence from a percept and
the time t at which it is experienced; and
which generates a declarative fact (in, again, the propositional calculus)
expressing that an action has been taken at some time t
Figure 6: A Typical Wumpus World
which give the agent the capacity to manipulate information in accordance
with the propositional calculus. (One step up from such an agent would
be a knowledge-based agent able to represent and reason over information
expressed in full first-order logic.) A colorful example of such an agent
is one clever enough to negotiate the so-called ``wumpus world." An example
of such a world is shown in Figure 6. The objective of the agent that finds
itself in this world is to find the gold and bring it back without getting
killed. As Figure 6 indicates, pits are always surrounded
on three sides by breezes, the wumpus is always surrounded on three sides
by a stench, and the gold glitters in the square in which it's positioned.
The agent dies if it enters a square with a pit in it (interpreted as
falling into a pit) or a wumpus in it (interpreted as succumbing to an
attack by the wumpus). The percepts for the agent can be given in the
form of quadruples. For example,
means that the agent, in the square in which it's located, perceives
a stench, a breeze, a glitter, and no scream. A scream occurs when the
agent shoots an arrow that kills the wumpus. There are a number of other
details involved, but this is enough to demonstrate how command over the
propositional calculus can give an agent a level of intelligence that
will allow it to succeed in the wumpus world. For the demonstration, let
Si,j represent the fact that there is
a stench in column i row j, let Bi,j
denote that there is a breeze in column i row j, and let
Wi,j denote that there is a wumpus in
column i row j. Suppose now that an agent has the following
5 facts in its KB.
Then in light of the fact that
in the propositional calculus,7 the agent can come to know (=
come to include in its KB) that the wumpus is at location column
1 row 3 -- and this sort of knowledge should directly contribute to the
In my lab, a number of students have built actual wumpus-world-winning
robots; for a picture of one toiling in this world see Figure 7.
Now I have no problem believing that the techniques and formalisms that
constitute the agent-based approach preached in AIMA are sufficient
to allow for the construction of characters that operate at the level
of animals. But when we reach the level of personhood, all bets, by my
lights, are off.
A Real-Life Wumpus-World-Winning Robot in the Minds & Machines
Laboratory (Observant readers may note that the wumpus here is represented
by a figurine upon which appears the (modified) face of the Director
of the M &M Lab: Bringsjord.)
Why is it that intelligent agent techniques will allow us to build virtual
rats, parrot, and chimps, but fail when we attempt to build virtual persons?
They will fail because intelligent agent architectures, formalisms, tools,
and so on are impotent in the face of the properties that distinguish
persons. What are these properties? Many philosophers have taken up the
challenge of answering this question, but for present purposes it suffices
to call upon an account of personhood offered in (Bringsjord, 1997); in fact, it suffices
to list here only five of the properties offered in that account, viz.,8
- 1. ability to communicate in a language
- 2. autonomy (``free will")
- 3. creativity
- 4. phenomenal consciousness
- 5. robust abstract reasoning (e.g., ability to create conceptual schemes,
and to switch from one to another)
For the sake of argument I'm prepared to follow Turing and hold that
AI will engineer not only the communicative powers of a parrot and a chimp,
but also the linguistic powers of a human person. (This concession requires
considerable optimism: The current state-of-the art in AI is unable to
create a device with the linguistic capacity of a toddler.) However, it's
exceedingly hard to see how each of the four remaining properties can
be reduced to the machinery of the intelligent agent paradigm in AI. Intelligent
agents don't seem to originate anything; they seem to do just what they
have been designed to do. And so it's hard to see how they can originate
decisions and actions (``free will") or artifacts (creativity). At least
at present, it's hard to see how phenomenal consciousness can be captured
in any third-person scheme whatever (and as many readers will know, a
number of philosophers -- Nagel, e.g. -- have argued that such consciousness
can never be captured in such a scheme), let alone in something as austere
as what AI engineers work with. And those in AI who seek to model abstract
reasoning know well that we have only begun to show how sophisticated
abstract reasoning can be cast in well-understood computable logics. For
all we know at present, it may be that some of this reasoning is beyond
the reach of computation. Certainly such reasoning cannot be cashed out
in the vocabulary of AIMA, which stays firmly within extensional
But let's focus, as I said I would, on the issue of autonomous intelligent
agents. I believe I have a way of sharpening the challenge that this issue
presents to those who aspire to create dramatically compelling interactive
electronic entertainment. This way involves subjecting would-be autonomous
virtual characters to a form of the Lovelace Test. But first, I have to
introduce the test.
As you probably know, Turing predicted in his famous ``Computing Machinery
and Intelligence" (1964) that by the turn of the century computers
would be so smart that when talking to them from a distance (via email,
if you will) we would not be able to tell them from humans: they would
be able to pass what is now known as the Turing Test (TT). Well, New Year's
Eve of 1999 has come and gone, all the celebratory pyrotechnics have died,
and the fact is: AI hasn't managed to produce a computer with the conversational
punch of a toddler.
But the really depressing thing is that though progress toward Turing's
dream is being made, it's coming only on the strength of clever but shallow
trickery. For example, the human creators of artificial agents that compete
in present-day versions of TT know all too well that they have merely
tried to fool those people who interact with their agents into
believing that these agents really have minds. In such scenarios it's
really the human creators against the human judges; the intervening computation
is in many ways simply along for the ride.
It seems to me that a better test is one that insists on a certain restrictive
epistemic relation between a an artificial agent A, its output
o, and the human architect H of S -- a relation which,
roughly speaking, obtains when H cannot account for how A
produced o. I call this test the ``Lovelace Test" in honor of Lady
Lovelace, who believed that only when computers originate things
should they be believed to have minds.
To begin to see how LT works, we start with a scenario that is close
to home for Bringsjord and Ferrucci, given their sustained efforts to
build story generation agents: Assume that Jones, a human AInik, attempts
to build an artificial computational agent A that doesn't engage
in conversation, but rather creates stories -- creates in the Lovelacean
sense that this system originates stories. Assume that Jones
activates A and that a stunningly belletristic story o is
produced. We claim that if Jones cannot explain how o was gemerated
by A, and if Jones has no reason whatever to believe that
A succeeded on the strength of a fluke hardware error, etc. (which
entails that A can produce other equally impressive stories), then
A should at least provisionally be regarded genuinely creative.
An artificial computational agent passes LT if and only if it stands to
its creator as A stands to Jones.
LT relies on the special epistemic relationship that exists between Jones
and A. But `Jones,' like `A,' is of course just an uninformative
variable standing in for any human system designer. This yields the following
- DefLT 1
- Artificial agent A, designed by H, passes LT if and
- 1 A outputs o;
- 2 A's outputting o is not the result
of a fluke hardware error, but rather the result of processes A
- 3 H (or someone who knows what H
knows, and has H's resources9) cannot explain how A
Notice that LT is actually what might be called a meta-test.
The idea is that this scheme can be deployed for any partcular domain.
If conversation is the kind of behavior wanted, then merely stipulate
that o is an English sentence (or sequence of such sentences) in
the context of a converation (as in, of course, TT). If the production
of a mathematical proof with respect to a given conjecture is what's desired,
then we merely set o to a proof. In light of this, we can focus
LT on the particular kind of interaction appropriate for the digital entertainment
Obvious questions arise at this point. Three are:
Q1 What resources and knowledge does H have at
his or her disposal?
Q2 What sort of thing would count as a successful explanation?
Q3 How long does H have to cook up the explanation?
The answer to the third question is easy: H can have as long as
he or she likes, within reason. The proffered explanation doesn't have
to come immediately: H can take a month, months, even a year or
two. Anything longer than a couple of years strikes us as perhaps unreasonable.
We realize that these temporal parameters aren't exactly precise, but
then again we should not be held to standards higher than those pressed
against Turing and those who promote his test and variants thereof.10 The general point, obviously,
is that H should have more than ample time to sort things out.
But what about Q1 and Q2? The answer to Q1 is that H is assumed
to have at her disposal knowledge of the architecture of the agent in
question, knowledge of the KB of the agent, knowledge of how the main
functions in the agent are implemented (e.g., how TELL
and ASK are implemented), and so on (recall the summary
of intelligent agents above). H is also assumed to have resources
sufficient to pin down these elements, to ``freeze" them and inspect them,
and so on. I confess that this isn't exactly precise. To clarify things,
I offer an example. This example is also designed to provide an answer
To fix the context for the example, suppose that the output from our
artificial agent A' is a resolution-based proof which settles a
problem which human mathematicians and logicians have grappled unsuccessfully
with for decades. This problem, suppose, is to determine whether or not
some formula can
be derived from some (consistent) axiom set . Imagine that after many
years of fruitless deliberation, a human H' encodes and and gives both to OTTER (a well-known theorem prover;
it's discussed in (Bringsjord & Ferrucci, 2000)), and OTTER
produces a proof showing that this encoding is inconsistent, which establishes
, and leads to an explosion of commentary in the media about ``brilliant"
and ``creative" machines, and so on.11 In this case, A' doesn't
pass LT. This is true because H, knowing the KB, architecture,
and central functions of A' will be able to give a perfect explanation
for the behavior in question. I routinely give explanations of this sort.
The KB is simply the encoding of , the architecture consists in the search algorithms used by OTTER,
and the main functions consist in the rules of inference used in a resolution-based
Here, now, given the foregoing, is a better definition:
- DefLT 2
Artificial agent A, designed by H, passes LT if and
- 1 A outputs o;
- 2 A's outputting o is not the result
of a fluke hardware error, but rather the result of processes A
- 3 H (or someone who knows what H
knows, and has H's resources) cannot explain how A
produced o by appeal to A's architecture, knowledge-base,
and core functions.
Today's systems, even those designed to either be, or seem to be, autonomous,
fail LT. These designers can imagine themselves generating the output
in question by merely manipulating symbols in accordance with the knowledge
bases, algorithms, and code in question. We give an example of this kind
of failure, an example that falls rather close to home for me.
The BRUTUS system is designed to appear to be literarily
creative to others. To put the point in the spirit of the Turing
Test, BRUTUS reflects a multi-year attempt to build a system
able to play the short short story game, or S3G for short (Bringsjord, 1998). (See Figure 8 for a picture of S3G.)
Figure 8: The Short Short Story Game, or S3G
The idea behind S3G is simple. A human and a computer compete
against each other. Both receive one relatively simple sentence, say:
``As Gregor Samsa awoke one morning from uneasy dreams he found himself
transformed in his bed into a gigantic insect." (Kafka 1948, p. 67) Both mind and
machine must now fashion a short short story (about 500 words) designed
to be truly interesting; the more literary virtue, the better. The goal
in building BRUTUS, then, is to build an artificial author
able to compete with first-rate human authors in S3G, much
as Deep Blue went head to head with Kasparov.
How does BRUTUS fare? Relative to the goal of passing
S3G, not very well. On the other hand, BRUTUS
can ``author" some rather interesting stories (Bringsjord & Ferrucci, 2000). Note that
we have placed the term `author' in scare quotes. Why? The reason is plain
and simple, and takes us back to Lady Lovelace's objection: BRUTUS
doesn't originate stories. He is capable of generating it because
two humans, Bringsjord and Ferrucci, spent years figuring out how to formalize
a generative capacity sufficient to produce this and other stories, and
they then are able to implement part of this formalization so as to have
a computer produce such prose. This method is known as reverse engineering.
Obviously, with BRUTUS set to A and Bringsjord and
Ferrucci set to H in the definition of LT, the result is that BRUTUS
fails this test.
Let's now give you, briefly, a specific example to make this failure
transparent. BRUTUS is programmed to produce stories that,
are, at least to some degree, bizarre. The reason for this is that reader
response research tells us that readers are engaged by bizarre material.
Now, in BRUTUS, to express the bizarre, modifiers are linked
with objects in frames named bizzaro_modifiers. Consider the
following instance describing the bizzaro modifier bleeding.
What Bringsjord and Ferrucci call literary augmented grammars,
or just a LAGs, may be augmented with constraints to stimulate bizarre
images in the mind of the reader. The following LAG for action analogies,
- BizarreActionAnalogy NP VP like ANP
- NP noun_phrase
- ANP modifier (isa bizzaro_modifier) noun (isa
analog of NP)
in conjunction with bizzaro_modifiers, can be used by BRUTUS
to generate the following sentence.
Hart's eyes were like big bleeding suns.
Sentences like this in output from BRUTUS are therefore
a function of work carried out by (in this case) Ferrucci. Such sentences
do not result from BRUTUS thinking on its own.
What does the Lovelace Test buy us? What role does it play in connection
with challenge C3? The overall idea is this. A truly autonomous virtual
character is an intelligent agent that has those attributes constitutive
of personhood, attributes that include autonomy. Operationalized, this
means that truly autonomous virtual characters would pass LT. But such
agents can't pass LT. Put in the form of an argument, and tied
to the question that gives this paper its title, we have:
The Argument That Worries Me
- 1. Dramatically compelling interactive digital entertainment requires
the presence in such entertainment of virtual persons, and therefore
requires the presence of autonomous virtual characters.
- 2. Autonomous virtual characters would pass the Lovelace Test.
- 3. Autonomous virtual characters would be intelligent agents, in the
technical sense of ``intelligent agents" in use in AI (specifically
- 4. Intelligent agents fail the Lovelace Test.
- 5. Dramatically compelling interactive digital entertainment isn't
What should the response be to this argument be? Perhaps you'll need
to think about what your own reaction should be; the point of this paper
is only to place the argument before you. Clearly, the argument is formally
valid, that is, the logic is correct: 5. does follow from the premises.
So to escape the argument, at least one of the premises must be rejected.
My suspicion is that premise 1. is false, but that what's true is
a relative, viz.,
- 1'. Dramatically compelling interactive digital entertainment
requires the presence in such entertainment of seemingly autonomous
If this is right, those who design and build digital entertainment need,
at bottom, to figure out ingenious ways of fooling players into believing
that virtual characters are, in general, persons (and hence, among other
things, autonomous). The job description for those intent on building
dramatically compelling interative digital entertainment thus calls for
those who can figure out the stimuli that impel gamers to believe they
are interacting with virtual people, and then engineer a system to produce
this stimuli in a principled way. This job description is decidely not
filled by those in game development who have mastered the so-called present-day
``art of character design," which is nicely summarized, e.g., in (Gard, 2000). Why this is so, and
what, as a practical engineering matter, needs to be done to extend present-day
techniques -- well, this will need to wait for another day.
the top of the page]
* This article was first presented as a paper
at the 2001 Computer Games &
Digital Textualities conference in Copenhagen.
I'm indebted to Dave Ferrucci, Devin Croak, and Marc Destefano.
Playing soccer against someone with Playstation 2 running to a large
plasma display is, for me, fun. Perhaps you, on the other hand, prefer
a current online multi-player game or two.
I have recently argued that deciding whether some story is interesting
is a computationally unsolvable problem. See ``Chapter 5: The Narrative-Based
Refutation of Church's Thesis" in (Bringsjord & Ferrucci, 2000).
The connectionist-logicist clash in AI is discussed in: (Bringsjord & Ferrucci, 1998,2000; Bringsjord, 1991). The last of these
publications constitutes an introduction to logicist AI.
A character is doxastically sophisticated if it can reason over
its beliefs about the beliefs other characters and human users have
about beliefs, etc.
The cycle here is strikingly similar to the overall architecture of
cognition described by Pollock (1995).
The proof is left to sedulous readers.
The account is streamlined in the interests of space. For example,
because people sleep (and because they can be hypnotized, etc.), a
person would be a creature with the capacity to have properties
like those listed here.
For example, the substitute for H might be a scientist who
watched and assimilated what the designers and builders of A
did every step along the way.
In (Bringsjord, 1995), Bringsjord refutes propositions
associated with TT by assuming for the sake of argument that some reasonable
parameters have been established
for this test. But Turing didn't specify , and neither
have his present-day defenders.
For a ``real life" counterpart, we have OTTER's settling
the Robbins Problem, presented as an open question in (Wos, 1996).
- Bringsjord, S. (1991),
`Is the connectionist-logicist clash one of ai's wonderful red herrings?',
Journal of Experimental & Theoretical AI 3.4, 319-349.
- Bringsjord, S. (1995),
Could, how could we tell if, and why should-androids have inner
lives?, in K. Ford, C. Glymour & P. Hayes,
eds, `Android Epistemology', MIT Press, Cambridge, MA, pp. 93-122.
- Bringsjord, S. (1997),
Abortion: A Dialogue, Hackett, Indianapolis, IN.
- Bringsjord, S. (1998),
`Chess is too easy', Technology Review 101(2), 23-28.
- Bringsjord, S. & Ferrucci, D. ( 1998),
`Logic and artificial intelligence: Divorced, still married, separated...?',
Minds and Machines 8, 273-308.
- Bringsjord, S. & Ferrucci, D. ( 2000),
Artificial Intelligence and Literary Creativity: Inside the
Mind of Brutus, a Storytelling Machine, Lawrence Erlbaum, Mahwah,
- Gard, T. (2000),
`Building character', Game Developer Magazine 7.5, 28-37.
- Kafka, F. (1948),
The metamorphosis, in F. Kafka, t. W. Muir &
E. Muir, eds, `The Penal Colony', Schocken Books, New York, NY.
- Pollock, J. (1995),
Cognitive Carpentry: A Blueprint for How to Build a Person,
MIT Press, Cambridge, MA.
- Russell, S. & Norvig, P. (1994),
Artificial Intelligence: A Modern Approach, Prentice Hall,
Saddle River, NJ.
- Turing, A. (1964),
Computing machinery and intelligence, in A. R. Anderson,
ed., `Minds and Machines', Prentice-Hall, Englewood Cliffs, NJ, pp. 4-30.
- Wos, L. (1996),
The Automation of Reasoning: An Experimenter's Notebook with
OTTER Tutorial, Academic Press, San Diego, CA.