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What has AI in Common with Philosophy?
Computer Science Department
Stanford, CA 94305, U.S.A.
February 29, 1996
AI needs many ideas that have hitherto been studied only by philoso-
phers. This is because a robot, if it is to have human level intelligence
and ability to learn from its experience, needs a general world view in
which to organize facts. It turns out that many philosophical problems
take new forms when thought about in terms of how to design a robot.
Some approaches to philosophy are helpful and others are not.
Artiﬁcial intelligence and philosophy have more in common than a science usu-
ally has with the philosophy of that science. This is because human level artiﬁ-
cial intelligence requires equipping a computer program with some philosophical
attitudes, especially epistemological.
The program must have built into it a concept of what knowledge is and
how it is obtained.
If the program is to reason about what it can and cannot do, its designers
will need an attitude to free will. If it is to do meta-level reasoning about what
it can do, it needs an attitude of its own to free will.
If the program is to be protected from performing unethical actions, its
designers will have to build in an attitude about that.
Unfortunately, in none of these areas is there any philosophical attitude
or system suﬃciently well deﬁned to provide the basis of a usable computer
Most AI work today does not require any philosophy, because the system
being developed doesn’t have to operate independently in the world and have a
view of the world. The designer of the program does the philosophy in advance
and builds a restricted representation into the program.
Building a chess program requires no philosophy, and Mycin recommended
treatments for bacterial infections without even having a notion of processes
taking place in time. However, the performance of Mycin-like programs and
chess programs is limited by their lack of common sense and philosophy, and
many applications will require a lot. For example, robots that do what they
think their owners want will have to reason about wants.
Not all philosophical positions are compatible with what has to be built into
intelligent programs. Here are some of the philosophical attitudes that seem to
me to be required.
at the intermediate size level on which humans operate without having to
understand fundamental physics. Causal relations must also be used for
a robot to reason about the consequences of its possible actions.
do extensive introspection. Contrast this with the attitude that unless a
system has a whole raft of features it isn’t a mind and therefore it can’t
meaningful. It is still possible to use mathematical logic to express ap-
quences of the available choices. These choices are present in its con-
sciousness and can give rise to sentences about them as they are observed.
2 The Philosophy of Artiﬁcial Intelligence
One can expect there to be an academic subject called the philosophy of arti-
ﬁcial intelligence analogous to the existing ﬁelds of philosophy of physics and
philosophy of biology. By analogy it will be a philosophical study of the re-
search methods of AI and will propose to clarify philosophical problems raised.
I suppose it will take up the methodological issues raised by Hubert Dreyfus and
John Searle, even the idea that intelligence requires that the system be made of
Presumably some philosophers of AI will do battle with the idea that AI
is impossible (Dreyfus), that it is immoral (Weizenbaum) and that the very
concept is incoherent (Searle).
It is unlikely to have any more eﬀect on the practice of AI research than
philosophy of science generally has on the practice of science.
3 Epistemological Adequacy
Formalisms for representing facts about the world have to be adequate for rep-
resenting the information actually available. A formalism that represented the
state of the world by the positions and velocities of molecules is inadequate if
the system can’t observe positions and velocities, although such a formalism
may be the best for deriving thermodynamic laws.
The common sense world needs a language to describe objects, their relations
and their changes quite diﬀerent from that used in physics and engineering. The
key diﬀerence is that the information is less complete. It needs to express what is
actually known that can permit a robot to determine the expected consequences
of the actions it contemplates.
4 Free Will
An attitude toward the free will problem needs to be built into robots in which
the robot can regard itself as having choices to make, i.e. as having free will.
5 Natural Kinds
Natural kinds are described rather than deﬁned. We have learned about lemons
and experienced them as small, yellow fruit. However, this knowledge does not
permit an iﬀ deﬁnition. Lemons diﬀer from other fruit in ways we don’t yet
know about. There is no continuous gradation from lemons to oranges. On the
other hand, geneticists could manage to breed large blue lemons by tinkering
with the genes, and there might be good reasons to call the resulting fruit
6 Four Stances
Daniel Dennett named three stances one can take towards an object or system.
The ﬁrst is the physical stance in which the physical structure of the system is
treated. The second is the intentional stance in which the system is understood
in terms of its beliefs, goals and intentions. The third is the design stance in
which the system is understood in terms of its composition out of parts. One
more stance we’ll call the functional stance. We take the functional stance
toward an object when we ask what it does without regard to its physics or
composition. The example I like to give is a motel alarm clock. The user may
not notice whether it is mechanical, an electric motor timed by the power line
or electronic timed by a quartz crystal.1 Each stance is appropriate in certain
7 Ontology and Reiﬁcation
Quine wrote that one’s ontology coincides with the ranges of the variables in
one’s formalism. This usage is entirely appropriate for AI. Present philosophers,
Quine perhaps included, are often too stingy in the reiﬁcations they permit. It
is sometimes necessary to quantify over beliefs, hopes and goals.
When programs interact with people or other programs they often perform
speech acts in the sense studied by Austin and Searle. Quantiﬁcation over
promises, obligations, questions, answers to questions, oﬀers, acceptances and
declinations are required.
An intelligent program will have to use counterfactual conditional sentences, but
AI needs to concentrate on useful counterfactuals. An example is “If another car
had come over the hill when you passed just now, there would have been a head-
on collision.” Believing this counterfactual might change one’s driving habits,
whereas the corresponding material conditional, obviously true in view of the
false antecedent, could have no such eﬀect. Counterfactuals permit systems to
learn from experiences they don’t actually have.
Unfortunately, the Stalnaker-Lewis closest possible world model of counter-
factuals doesn’t seem helpful in building programs that can formulate and use
9 Philosophical Pitfalls
There is one philosophical view that is attractive to people doing AI but which
limits what can be accomplished. This is logical positivism which tempts AI
1I had called this the design stance, and I thank Aaron Sloman for pointing out my mistake
and suggesting functional stance.
people to make systems that describe the world in terms of relations between the
program’s motor actions and its subsequent observations. Particular situations
are sometimes simple enough to admit such relations, but a system that only
uses them will not even be able to represent facts about simple physical objects.
It cannot have the capability of a two week old baby.
10 Philosophers! Help!
Previous philosophical discussion of certain conecpts has been helpful to AI. In
this I include the Austin-Searle discussion of speech acts, Grice’s discussion of
conversational implicatures, various discussions of natural kinds, modal logic
and the notion of philosophy as a science. Maybe some of the philosophical dis-
cussions of causality and counterfactuals will be useful for AI. In this paragraph
I have chosen to be stingy with credit.
Philosophers could help artiﬁcial intelligence more than they have done if
they would put some attention to some more detailed conceptual problems such
as the following:
belief What belief statements are useful?
how What is the relation between naming an occurrence and its suboccur-
rences? He went to Boston. How? He drove to the airport, parked and
took UA 34.
responsiveness When is the answer to a question responsive? Thus “Vladimir’s
wife’s husband’s telephone number” is a true but not responsive answer to
a request for Vladimir’s telephone number.
useful causality What causal statements are useful?
useful counterfactuals What counterfactuals are useful and why? “If an-
other car had come over the hill when you passed, there would have been
a head-on collision.”
There is not space in this article nor have I had the time to prepare a proper
bibliography. Such a bibliography would refer to a number of papers, some
of mine being reprinted in my Formalizing Common Sense Many are available
via my Web page http://www-formal.stanford.edu/jmc/. I would also refer to
work by the following philosophers: Rudolf Carnap, Daniel Dennett, W. V. O.
Quine, Hilary Putnam, Paul Grice, John Searle, Robert Stalnaker, David Lewis,
Aaron Sloman, Richard von Mises. Much of the bibliography in Aaron Sloman’s
previous article is applicable to this one.
Acknowledgement: Work partly supported by ARPA (ONR) grant N00014-