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\title{What has AI in Common with Philosophy?}
\author{John McCarthy\\
Computer Science Department\\
Stanford University\\
Stanford, CA 94305, U.S.A.\\[0.15cm]
jmc@cs.stanford.edu, http://www-formal.stanford.edu/jmc/}



\begin{document}

\maketitle

\begin{abstract}
AI needs many ideas that have hitherto been studied only by
philosophers.  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.

\end{abstract}

\section{Introduction}
\label{sec:Introduction}

Artificial intelligence and philosophy have more in common than a
science usually has with the philosophy of that science.  This is
because human level artificial 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 
\emph{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 program.

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.

\begin{enumerate}

\item Science and common sense knowledge of the world must both be
  accepted.  There are atoms, and there are chairs.  We can learn
  features of the world 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.

\item Mind has to be understood a feature at a time.  There are
  systems with only a few beliefs and no belief that they have
  beliefs.  Other systems will 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 have
  beliefs.

\item Beliefs and intentions are objects that can be formally
  described.
  
\item A sufficient reason to ascribe a mental quality is that it 
accounts for behavior to a sufficient degree.

\item It is legitimate to use approximate concepts not capable of
  {\bf iff\/} definition.  For this it is necessary to relax some of
  the criteria for a concept to be meaningful.  It is still possible
  to use mathematical logic to express approximate concepts.
  
\item Because a theory of approximate concepts and approximate 
  theories is not available, philosophical attempts to be precise 
  have often led to useless hair splitting.
  
\item Free will and determinism are compatible.  The deterministic 
process that determines what an agent will do involves its evaluation 
of the consequences of the available choices.  These choices are 
present in its consciousness and can give rise to sentences about them 
as they are observed.

\item Self-consciousness consists in putting sentences about 
consciousness in memory.

\item Twentieth century philosophers became to critical of 
reification.  Many of the criticism don't apply when the entities 
reified are treated as approximate concepts.

\end{enumerate}

\section{The Philosophy of Artificial Intelligence}
\label{sec:philosophy}

One can expect there to be an academic subject called the philosophy
of artificial intelligence analogous to the existing fields of
philosophy of physics and philosophy of biology.  By analogy it will
be a philosophical study of the research 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 meat.

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.

\section{Epistemological Adequacy}
\label{sec:epistemological}

Formalisms for representing facts about the world have to be adequate 
for representing 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.

\section{Free Will}
\label{sec:free}
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.

\section{Natural Kinds}
\label{sec:natural}
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 {\bf 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
lemons.

\section{Four Stances}
\label{sec:stances}
Daniel Dennett named three {\em stances\/} one can take towards an
object or system.  The first is the {\em physical stance\/} in which
the physical structure of the system is treated.  The second is the
{\em intentional stance\/} in which the system is understood in terms
of its beliefs, goals and intentions.  The third is the {\em design
  stance\/} in which the system is understood in terms of its
composition out of parts.  One more stance we'll call the {\em
  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.\footnote{I had called
  this the design stance, and I thank Aaron Sloman for pointing out my
  mistake and suggesting {\em functional stance}.} Each stance is
appropriate in certain conditions.

\section{Ontology and Reification}
\label{sec: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 {\em speech acts} in the sense studied by Austin and Searle.
Quantification over promises, obligations, questions, answers to
questions, offers, acceptances and declinations are required.

\section{Counterfactuals}
\label{sec:counterfactuals}
An intelligent program will have to use counterfactual conditional
sentences, but AI needs to concentrate on useful counterfactuals.  An
example is {\em ``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
counterfactuals doesn't seem helpful in building programs that can
formulate and use them.  

\section{Philosophical Pitfalls}
\label{sec: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 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.

\section{Philosophers!  Help!}
\label{sec: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 discussions 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:

\begin{description}

\item[belief] What belief statements are useful?

\item[how] What is the relation between naming an occurrence and its
  suboccurrences?  {\em He went to Boston.  How?  He drove to the airport,
  parked and took UA 34.\/}

\item[responsiveness] When is the answer to a question responsive?
  Thus {\em ``Vladimir's wife's husband's telephone number''\/} is a
  true but not responsive answer to a request for Vladimir's telephone
  number.

\item[useful causality] What causal statements are useful?

\item[useful counterfactuals] What counterfactuals are useful and why?
  {\em ``If another car had come over the hill when you passed, there would
  have been a head-on collision.''\/}

\end{description}

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\section*{References}
\label{sec:references}

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 {\em 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 N00014-94-1-0775.

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\end{document}
What can AI get from philosophy? natural kinds, Gricean implicatures,
philosophy as a science
