COMMENT ⊗   VALID 00014 PAGES
C REC  PAGE   DESCRIPTION
C00001 00001
C00003 00002	%thomas[e88,jmc]		Another try at AI and logic for Thomason
C00004 00003	\section{Introduction}
C00028 00004	\section{The Common Sense Informatic Situation}
C00033 00005	\section{Some formalizations and their problems}
C00036 00006	\section{What new formal tools does AI require?}
C00037 00007	\noindent Rich and poor entities
C00040 00008	\noindent Knowledge and Belief
C00044 00009	\noindent Meta-epistemology
C00051 00010	\section{Philosophical problems raised by AI}
C00054 00011	\noindent Ontology
C00057 00012	\section{References}
C00058 00013	\section{Notes: not for final version}
C00062 00014	\smallskip\centerline{Copyright \copyright\ \number\year\ by John McCarthy}
C00065 ENDMK
C⊗;
%thomas[e88,jmc]		Another try at AI and logic for Thomason
\input memo.tex[let,jmc]
\title{Artificial Intelligence, Logic and Formalizing Common Sense}

\section{Introduction}

	This is a position paper about the relations among artificial
intelligence (AI), mathematical logic and the formalization of common sense
knowledge and reasoning.  The actual formalisms (mostly first order
languages) for expressing common sense facts are described in the
references.

	One path to human-level AI is formalizing common sense
knowledge and reasoning in mathematical logic.  This article discusses
the problems and difficulties, the results so far, and the
improvements in logic and logical languages required to realize the
goal.  Fundamental conceptual advances are almost certainly required.
The object of the paper is to get more help for AI from philosophical
logicians.  Some of the requested help will be mostly philosophical
and some will be logical.  Likewise the concrete AI approach may
fertilize philosophical logic as physics has repeatedly fertilized
mathematics.

	There are two reasons for emphasizing common sense knowledge
rather than the knowledge contained in scientific theories.

	Scientific theories represent compartmentalized knowledge.  In
presenting a scientific theory, as well as in developing it, there is
a common sense pre-scientific stage.  In this stage, it is decided or
just stated what phenomena are taken into account and what is the
relation between certain formal terms of the theory and the common
sense world.  Thus in classical mechanics it is decided what kinds of
bodies and forces are to be used before the differential equations are
written down.  In probabilistic theories, the sample space is
determined.  In theories expressed in first order logic, the predicate
and function symbols are decided upon.  The axiomatic reasoning
techniques used in mathematical and logical theories depend on this
having been done.  However, a robot or computer program with
human-level intelligence will have to do this for itself.  To use
science, common sense is required.

	The second reason is that common sense reasoning is required
for solving problems in the common sense world.  The common sense
world, from the problem solving or goal-achieving point of view is
characterized by a different {\it informatic situation} than that
{\it within} any formal scientific theory.  In the common sense informatic
situation, the reasoner doesn't know what facts are relevant to
solving his problem.  Unanticipated obstacles may arise that involve
using parts of his knowledge not previously thought to be relevant.

	It might be supposed that the common sense world would admit a
conventional scientific theory, e.g. a probabilistic theory.  No-one
has developed it, and AI has taken a somewhat different course that
involves nonmonotonic extensions to the kind of reasoning used in
formal scientific theories.  This seems to us likely to work better.

	Aristotle, Leibniz, Boole and Frege all had common sense
knowledge in mind when they discussed formal logic.  However,
formalizing much of common sense knowledge and reasoning proved
elusive, and the twentieth century emphasis has been on formalizing
mathematics.  Some important philosophers, e.g. Wittgenstein,
 have mistakenly claimed that
common sense knowledge is unformalizable or mathematical logic is
inappropriate for doing it.  This is partly a consequence of the
inadequacy of the specific collections of predicates and functions
they took as examples, partly from not formalizing nonmonotonic
reasoning, and most likely for lack of logical tools still to be
discovered.  While I acknowledge this opinion, I haven't the time or
the scholarship to deal with the full range of such arguments.
Instead I will present the positive case, the problems that have
arisen, what has been done and the problems that can be forseen.

	If a computer is to store facts about the world and reason
with them, it needs a precise language, and the program has to embody
a precise idea of what reasoning is allowed, i.e. of how new formulas
may be derived from old.  Therefore, it was natural to try to use
mathematical logical languages to express what an intelligent computer
program knows that is relevant to the problems we want it to solve and
to make the program use logical inference in order to decide what to
do.  (McCarthy 1960) contains the first proposals to use logic in AI
for expressing what a program knows and and how it should reason.
(Proving logical formulas as a domain for AI had already been
studied).

	The 1960 paper said:

\begingroup\narrower\narrower
% COMMON.TEX[E80,JMC] TeX version Programs with Common Sense
%
The {\it advice taker} is a proposed program for solving problems by
manipulating sentences in formal languages.  The main difference
between it and other programs or proposed programs for manipulating
formal languages (the {\it Logic Theory Machine} of Newell, Simon and
Shaw and the Geometry Program of Gelernter) is that in the previous
programs the formal system was the subject matter but the heuristics
were all embodied in the program.  In this program the procedures will
be described as much as possible in the language itself and, in
particular, the heuristics are all so described.

	The main advantages we expect the {\it advice taker} to have
is that its behavior will be improvable merely by making statements to
it, telling it about its symbolic environment and what is wanted from
it.  To make these statements will require little if any knowledge of
the program or the previous knowledge of the {\it advice taker}.  One
will be able to assume that the {\it advice taker} will have available
to it a fairly wide class of immediate logical consequence of anything
it is told and its previous knowledge.  This property is expected to
have much in common with what makes us describe certain humans as
having {\it common sense}.  We shall therefore say that {\it a program
has common sense if it automatically deduces for itself a sufficiently
wide class of immediate consequences of anything it is told and what
it already knows.}
\par\endgroup

	The main reasons for using logical sentences extensively in AI
are better understood by researchers today than in 1960.  Expressing
information in declarative sentences is far more flexible than
expressing it in segments of computer program or in tables.  Sentences
can be true in much wider contexts than specific programs can be
useful.  The supplier of a fact does not have to understand much about
how the receiver functions or how or whether the receiver will use it.
The same fact can be used for many purposes, because the logical
consequences of collections of facts can be available.

	The {\it advice taker} prospectus was ambitious in 1960, would
be considered ambitious today and is still far from being immediately
realizable.  This is especially true of the goal of expressing the the
heuristics guiding the search for a way to achieve the goal in the
language itself.  The rest of this paper is largely concerned with
describing what progress has been made, what the obstacles are, and
how the prospectus has been modified in the light of what has been
discovered.

	The formalisms of logic have been used to differing extents,
mostly much less ambitious, and we'll begin by recounting some of
them.

	1. A machine may use no logical sentences --- all its
``beliefs'' being implicit in its state.  Nevertheless, it is often
appropriate to ascribe beliefs and goals to the program, i.e. to
remove the above sanitary quotes, and to use a principle of
rationality --- {\it It does what it thinks will achieve its goals}.
Such ascription is discussed in (Dennett 1971), (McCarthy 1979) and
(Newell 1980).  The advantage is that the intent of the machine's
designers and the way it can be expected to behave may be more readily
described {\it intentionally} than by a purely physical description.

The relation between the physical and the {\it intentional}
descriptions is most readily understood in simple systems that admit
readily understood descriptions of both kinds, e.g. thermostats.  Some
finicky philosophers object to this, contending that unless a system
has a full human mind, it shouldn't be regarded as having any mental
qualities at all.  This is like omitting the numbers 0 and 1 from the
number system on the grounds that numbers aren't required to count
sets with no elements or one element.  Much more that can be said
about ascribing mental qualities to machines, but that's not where the
main action is in AI.

	2. The next level of use of logic involves computer programs
that use sentences in machine memory to represent their beliefs but
use other rules than ordinary logical inference to reach conclusions.
New sentences are often obtained from the old ones by ad hoc programs.
Moreover, the sentences that appear in memory are from a
program-dependent subset of the logical language being used.  Adding
certain true sentences in the language may even spoil the functioning
of the program.  The languages used are often rather unexpressive
compared to first order logic, for example they may not admit
quantified sentences, and they may represent ``rules'', e.g. the
equivalent of universally quantified sentences in a separate notation.
Often rules cannot be consequences of the program's reasoning; they
must have all been put in by the ``knowledge engineer''.  Sometimes
the reason programs have this form is just ignorance, but the usual
reason for the restriction is the practical one of making the program
run fast and deduce just the kinds of conclusions its designer
anticipates.  Most often the rules are implications used in just one
direction, e.g. the contrapositive of an implication is not used.  We
believe the need for such specialized inference will turn out to be
temporary and will be reduced or eliminated by improved ways of
controlling general inference, e.g. by allowing the heuristic rules to
be also expressed as sentences as advocated in the above extract from
the 1960 paper.

	3. The third level uses first order logic and also logical
deduction.  Typically the sentences are represented as clauses, and the
deduction methods are based on J. Allen Robinson's (1965) method of
resolution.  It is common to use a theorem prover as a problem solver,
i.e.  to determine an $x$ such that $P(x)$ as a byproduct of a proof of
the formula $∃x.P(x)$ that represents the proposition that there is an
$x$ satisfying the predicate $P$.  This level is less used for practical
purposes than level two, because techniques for controlling the
reasoning are still insufficiently developed, and it is common for the
program to generate many useless conclusions before reaching the desired
solution.  Indeed unsuccessful experience (Green 1969) with this method
led to more restricted uses of logic, e.g. the STRIPS system of (Nilsson
and Fikes 1971).

	The commercial ``expert system shells'', e.g. ART, KEE and
OPS-5, use logical representation of facts, usually ground facts only,
and separate facts from rules.  They provide elaborate but not always
adequate ways of controlling inference.

	In this connection it is important to mention logic programming,
first introduced in Microplanner (Sussman et al., 1971) 
and from different points of view by Robert Kowalski (1979) and Alain
Colmerauer in the early 1970s.
A recent text is (Sterling and Shapiro 1986).  Microplanner
was a rather unsystematic collection of tools, whereas Prolog relies
almost entirely on one kind of logic programming, but the main idea
is the same.  If one uses a restricted class of sentences, the so-called
Horn clauses, then it is possible to use a restricted form of logical
deduction, and the control problem are much eased, and it is possible
for the programmer to anticipate the course the deduction will take.
The price paid is that only certain kinds of facts are conveniently
expressed as Horn clauses, and the depth first search built into
Prolog is not always appropriate for the problem.  Nevertheless,
expressibility in Horn clauses is an important property of a set
of facts and logic programming has been successfully used for many
applications, although it seems unlikely to dominate AI programming
as some of its advocates hope.

	Although  third level systems express both facts and rules
as logical sentences, they are still rather specialized.  The axioms
with which the programs begin are not general truths about the world
but are sentences whose meaning and truth is limited to the narrow
domain in which the program has to act.  For this reason, the ``facts''
of one program usually cannot be used in a database for other programs.

	4. The fourth level is still a goal.  It involves representing
general facts about the world as logical sentences.  Once put in
a database, the facts can be used by any program.  The facts would
have the neutrality of purpose characteristic of much human information.
The supplier of information would not have to understand
the goals of the potential user or how his mind works.  The present
ways of ``teaching'' computer programs amount to ``education
by brain surgery''.

	A major difficulty is that fourth level systems require extensions
to mathematical logic.  One kind of extension is nonmonotonic
reasoning, first proposed in the late 1970s (McCarthy 1977, 1980, 1986),
(Reiter 1980), (McDermott and Doyle 1980).  Traditional logic is monotonic
in the following sense.  If we have $A \vdash p$ and $A ⊂ B$, then we also
have $B \vdash p$.

	If the inference is logical deduction, then exactly the same
proof that proves $p$ from $A$ will serve as a proof from $B$. If the
inference is model-theoretic, i.e.  $p$ is true in all models of $A$,
then $p$ will be true in all models of $B$, because the models of $B$
will be a subset of the models of $A$.  So we see that the monotonic
character of traditional logic doesn't depend on the details of the
logical system but is quite fundamental.

	While much human reasoning corresponds to that of traditional
logic, some important human common sense reasoning is not monotonic.  We
reach conclusions from certain premisses that we would not reach if
certain other sentences were included in our premisses.  For example,
learning that I own a car, you conclude that it is appropriate on a
certain occasion to ask me for a ride, but when you learn the further
fact that the car is in the garage being fixed you no longer draw that
conclusion.  Some people think it is possible to try to save
monotonicity by saying that what was in your mind was not a general rule
about asking for a ride from car owners but a probabilistic rule.  So
far these people have not worke out any detailed
epistemology for this approach, i.e.  exactly what probabilistic
sentences should be used.  Instead AI has moved to directly formalizing
nonmonotonic logical reasoning.

	Nonmonotonic reasoning is extensively discussed in (McCarthy 1980
and 1986) and surveyed in (Lifschitz 1988).
\section{The Common Sense Informatic Situation}

	It's complex.

	1. Entities of interested are known only partially, and the
information about entities and their relations that may be relevant
to achieving goals cannot be permanently separated from irrelevant
information.  
%
(Contrast this with the situation in gravitational
astronomy in which it is stated in the informal introduction that
the chemical composition and shape of a body are irrelevant to the
theory; all that counts is the body's mass, and its initial position
and velocity).

	Even within gravitational astronomy, non-equational theories
arise.  For example, it was recently proposed that the periodic
extinctions are caused by showers of comets induced by a companion
star to the sun encountering and disrupting the Oort cloud of comets
every time it comes to perihelion.  This theory is qualitative because
neither the orbit of the hypothetical star nor those of the comets
is available.

	2. The formalism has to be {\it epistemologically adequate},
a notion introduced in (McCarthy and Hayes 1970).  This means that
the formalism must be capable of representing the information that
is actually available, not merely capable of representing actual
complete states of affairs.

	For example, it is insufficient to have a formalism that
can represent the positions and velocities of the particles in a
gas.  We can't obtain that information, our largest computers don't
have the memory to store were it available, and our fastest computers
couldn't use the information to make predictions.

	As a second example, suppose we need to be able to predict
someone's behavior.  The simplest example is a clerk in a store.
The clerk is a complex individual about whom a customer may know
little.  However, the clerk can usually be counted on to accept
money for articles brought to the counter, wrap them as appropriate
and not protest when the customer takes the articles from the store.
The clerk can also be counted on to object if the customer attempts
to take the articles without paying the appropriate price.  Describing
this requires a formalism capable of representing information about
human social institutions.  Moreover, the formalism must be capable
of representing partial information about the institution, such as
a three year old's knowledge of store clerks.  For example, a three
year old doesn't know the clerk is an employee or even what that
means.  It doesn't require detailed information about the clerk's
psychology, and this information is not ordinarily available.
\section{Some formalizations and their problems}

	(McCarthy 1986) discusses several formalizations, proposing
those based on nonmonotonic reasoning as improvements of earlier
ones.  Here are some.

	1. Inheritance with exceptions.  Birds normally fly, but there
are exceptions, e.g. ostriches and birds whose feet are encased in
concrete.  The first exception might be listed in advance, but the
second has to be derived or verified when mentioned on the basis of
information about the mechanism of flying and the properties of
concrete.

	There are many ways of nonmonotonically axiomatizing the
facts about which birds can fly.  The following axioms using
a predicate $ab$ standing for ``abnormal'' seem
to me quite straightforward.
%\leql{a4a:}
$$∀x.¬ab aspect1 x ⊃ ¬flies x.\leql{aiva}$$
%
Unless an object is abnormal in $aspect1$, it can't fly.  (We're using a
convention that parentheses may be omitted for functions and predicates
of one argument, so that (\eqref{aiva}) is the same as $∀x.(¬ab(aspect1(x))
 ⊃ ¬flies(x))$.)

	It wouldn't work to write $ab x$ instead of $ab aspect1 x$,
because we don't want a bird that is abnormal with respect to its ability
to fly to be automatically abnormal in other respects.  Using aspects limits
the effects of proofs of abnormality.
%leql{a5:}
$$∀x.bird x ⊃ ab aspect1 x.\leql{av}$$
%leql{a6:}
$$∀x.bird x ∧ ¬ab aspect2 x ⊃ flies x.\leql{avi}$$
%
Unless a bird is abnormal in $aspect2$, it can fly.
\section{What new formal tools does AI require?}

	1. Generality and elaboration tolerance require 

	1. Formalisms for specific phenomena must be imbedded
in general logical languages.  For example, facts  about
knowledge must be expressible in the same language as facts
about the effects of actions and other events, and such facts
must be allowed objects of knowledge.

\noindent Rich and poor entities

	Consider my next trip to Japan.  Considered as a plan it is
a discrete object with limited detail.  I do not yet even plan to
take a specific flight or to fly on a specific day.  Considered as
a future event, lots of questions may be asked about it.  For example,
it may be asked whether the flight will depart on time and what precisely
I will eat on the airplane.  We propose characterizing the actual trip
as a rich entity and the plan as a poor entity.  Originally, I thought
that rich events referred to the past and poor ones to the future, but
this seems to be wrong.  It's only that when one refers to the past
one is usually referring to a rich entity, while the future entities
one refers to are more often poor.  However, there is no intrinsic
association of this kind.

	(McCarthy and Hayes 1969) defines situations as rich entities.
However, the actual programs that have been written to reason in
situation calculus might as well regard them as taken from a
finite or countable set of discrete states.

	Rich entities are open ended in that we can always introduce
more properties of them into our discussion.  Poor entities can often
be enumerated, e.g. we can often enumerate all the events that we
consider reasonably likely in a situation.  The passage from considering
rich entities in a given discussion to considering poor entities is
a step of nonmonotonic reasoning.

	It seems to me that it is important to get a good formalization
of the relations between corresponding rich and poor entities.
This can be regarded as formalizing the relation between the world
and a formal model of some aspect of the world, e.g. between the
world and a scientific theory.

\noindent Knowledge and Belief

	Formalizing the facts about knowledge and belief has been
an active field ever since (Hintikka 1962).  Here are some of its
features.

	1. Most of the formalizations have taken the form of modal
propositional logics.  These are useful for studying properties of
knowledge and belief in isolation from other phenomena, but it isn't
apparent how to imbed them in a general common sense framework.  In such a
framework it should be possible to infer the plausibility of a plan to
hide something in order to prevent another person from finding out its
existence or location.

	Even the simple puzzle of the three wise men with spots
on their foreheads requires going beyond the simple modal formalisms.
It requires the ability to assert and prove non-knowledge.  It must
be assertable that the three wise men initially know only what the
king has told them.  Then it must be inferrable that after they have
answered whether they know the colors of their spots, they still don't
know the colors of their own spots.  This requires knowledge as a
function of time or of situations that result from events.

	2. The modal formalizations also treat only ``knowing that'',
omitting ``knowing what'', ``knowing how'' and ``knowing about''.
\noindent Meta-epistemology
% meta[s88,jmc]		Message to AILIST on metaepistemology
% meta[e85,jmc]		Meta-epistemology
% metaep[f82,jmc]		A proposal for meta-epistemology

	If we are to program a computer to think about its own
methods for gathering information about the world, then it needs
a language for expressing assertions about the relation between
the world, the information gathering methods available to an
information seeker and what it can learn.  This leads to a subject
I like to call meta-epistemology.  Besides its potential applications
to AI, I believe it has applications to philosophy considered in
the traditional sense.

	Meta-epistemology is proposed as a mathematical theory
in analogy to metamathematics.  Metamathematics considers the
mathematical properties of mathematical theories as objects.
In particular model theory as a branch of metamathematics deals
with the relation between theories in a language and interpretations
of the non-logical symbols of the language.  These interpretations
are considered as mathematical objects, and we are only sometimes
interested in a preferred or true interpretation.

	Meta-epistemology considers the relation between the world,
languages for making assertions about the world, notions of what
assertions are considered meanigful, what are accepted as rules
of evidence and what a knowledge seeker can discover about the
world.  All these entities are considered as mathematical objects.
In particular the world is considered as a parameter.
Thus meta-epistemology has the following characteristics.

	1. It is a purely mathematical theory.  Therefore, its
controversies, assuming there are any, will be mathematical
controversies rather than controversies about what the real world
is like.  At least metamathematics had the property of giving
many philosophical issues in the foundations of mathematics a
technical content.  For example, the theorem that intuitionist
arithmetic and Peano arithmetic are equi-consistent removed
at least one area of controversy between those whose mathematical
intuitions support one view of arithmetic or the other.

	2. While most modern philosophies of science assume some
relation between what is meaningful and what can be verified or
refuted, only special meta-epistemological systems will have the
corresponding mathematical property that all aspects of the world
relate to the experience of the knowledge seeker.

	This has several important consequences for the programming a
knowledge seeker.

	1. A knowledge seeker must have no a priori prejudices (principles)
about what concepts might be meaningful.  Whether and how a proposed concept
about the world
might ever connect with observation may remain in suspense for a very
long time will the concept is investigated and related to other concepts.

	We illustrate this by a literary example.  Moli\'re's play
 {\it La Malade Imaginaire} includes a doctor who explains sleeping
powders by saying that they contain a ``dormitive virtue''.  In the
play, the doctor is considered a pompous fool for offering a concept
that explains nothing.  However, suppose the doctor had some intuition
that the dormitive virtue might be extracted and concentrated, say
by shaking the powder in a mixture of ether and water.  Suppose he
thought that he would get the same concentrate from all substances
with soporific effect.  He would certainly have a fragment of
scientific theory subject to later verification.  Now suppose less ---
namely, he only believes that a common component is behind all
substances whose consumption makes one sleepy but has no idea
that he should try to invent a way of verifying the conjecture.
He still has something that, if communicated to someone more
scientifically minded, might be useful.  In the play, the doctor
obviously sins intellectually by claiming a hypothesis as certain.

	2. A knowledge seeker must be able to form new concepts
with only extremely tenuous relations with its previous linguistic
structure.
\section{Philosophical problems raised by AI}

	In this paper we will ignore the controversies about
whether the term ``artificial intelligence'' is a violation of
``logical grammar'' (Shanker 1988), whether intelligence is necessarily an
attribute of biological systems (Dreyfuss 198xx, Searle 198xx).
  I have expressed opinions
on these subjects in (McCarthy 19xx).  I will only say that
I have found the arguments difficult to understand in one
important respect --- whether the authors are making an assertion
about what performance human built robots will eventually have
or only about the proper language to use in characterizing that
performance.

	Practical experience and theoretical study of how to write
intelligent computer programs has led to the following propositions.
I don't claim to have proved them.

	1. Epistemology.  One studies what a program with given initial
knowledge and a given strategy for learning will find out about its
environment as a function of the environment, the strategy and what it
already knows.  This is the AI approach to epistemology.  Put in this
naive scientific way, the proposition seems obvious, but it is important
to note that it involves epistemological commitments.  There is a world
whose properties are not determined by those of the program learning about
it.  One studies the improvement of knowledge rather than starting with a
blank slate.

\noindent Ontology

	We accept Quine's idea that one's ontology is
expressed by the range of the variables in the formalism.  However,
it is not a priori obvious what the ontology should be.  In particular,
nominalistic arguments aimed at restricting the ontology are often
faulty, because of a lack of imagination about what entities need to
be reasoned about.  For example, we need propositions of some kind,
because we want to say about someone that he has some false beliefs
about a certain subject.  As another example, we need to be able to
say that there are two things wrong with the boat; it has a lack of
oars and it has a leak.  We need to enumerate things wrong with the
boat, so having a leak and water rushing in are one thing wrong and
not two.

	We need to distinguish between concepts specified in definitions
and natural kinds.  The aspect of natural kinds important here is that
an agent regards them as objective.  There is more to be learned about
what distinguishes objects of this kind from other objects.  It is also
important that there isn't in common experience a continuous gradation
of fruit between lemons and oranges.  This allows us, our children and
our programs to believe in the existence of a sharp distinction between
oranges and lemons while simultaneously being unable to give the
distinction.

	Here are some of the kinds of entities that have been used
in AI.

	1. Situations and states.

	2. Events

$$s' = result(e,s)$$
%
is the situation that results when the event $e$ occurs in the situation $s$.
\section{References}
\section{Notes: not for final version}

1. List of what AI needs

contexts, nonmonotonic, elaboraton tolerance, approximate theories
avoiding Montague's paradoxes of intentionality, rich entities,
frame and qualification problems, meta-epistemology
causality

2. slogans

modality si, modal logic no

3. Common sense database

Why common sense knowledge rather than scientific knowledge?
Russell quote.

``All philosophers, of every school, imagine that causation is one
of the fundamental axioms or postulates of science, yet, oddly enough,
in advanced sciences such as gravitational astronomy, the word `cause'
never occurs $\ldots$. The law of causality, I believe, like much that passes
muster among philosophers, is a relic of a bygone age, surviving, like the
monarchy, only because it is erroneously supposed to do no harm $ldots$.''
--- B. Russell, On the Notion of Cause, Proceedings of the Aristotelian 
 Society, 13 (1913), pp. 1-26.
Text actually reproduced from:
P. Suppes, ``A Probabilistic Account of Causation,''
 Acta Philosophica Fennica, Fasc. XXIV, North Holland, 1970.
-------
daedal.2[f87,jmc]

4. pedigrees

5. advice about reasoning

6. epistemological adequacy

7. frame problem,

Pylyshyn, Zenon W. (Ed.), {\it The Robot's Dilemma: The Frame Problem
in Artificial Intelligence} (Ablex, Norwood, NJ 1987)

Smoliar, Stephen W.Review of {\it The Robot's Dilemma}, {\it Artificial
Intelligence} vol 36 Number 1, August 1988

8. Avoid misunderstanding.  There is no objection to the delimitation
of what is taken into account.  That is necessary in common sense
reasoning also.  The point is that the delimitation itself must be
a formal process, e.g. circumscription.  Moreover, it must be reversible
if the limited theory proves inadequate.

Technical problems
Montagues paradoxes
Quantification under modal operators

Godel showed that provability could be formalized in principle, but he
didn't need to make it convenient, because his goals could be accomplished
by non-formalized meta-level reasoning about what formalized object level
reasoning would accomplish.

∃x believes(Ralph,spy x)
This is ok provided we consider the statement to be made in a context in
which there is some way of specifying what Ralph would take as a
concept of x.

nonmon needed to form proposition to be given a probability

\smallskip\centerline{Copyright \copyright\ \number\year\ by John McCarthy}
\smallskip\noindent{This draft of thomas[e88,jmc]\ TEXed on \jmcdate\ at \theTime}
\vfill\eject\end