This is one of the original papers that introduced LISP.
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LISP was invented back in the 60s to make implementing symbolic AI ...
This conditional operator lets you have any number of clauses, like...
He builds upon the work of Church. So we can attribute to this pape...
Would 'pair' here be equivalent to what in many languages would be ...
I didn't realize the first LISP system had garbage collection built...
I don't understand this section about L-expressions at all, what is...
Recursive Functions of Symbolic Expressions
and Their Computation by Machine, Part I
John McCarthy, Massachusetts Institute of Technology, Cambridge, Mass.
∗
April 1960
1 Introduction
A programming system called LISP (for LISt Processor) has been developed
for the IBM 704 computer by the Artificial Intelligence group at M.I.T. The
system was designed to facilitate experiments with a proposed system called
the Advice Taker, whereby a machine could be instructed to handle declarative
as well as imperative sentences and could exhibit “common sense” in carrying
out its instructions. The original proposal [1] for the Advice Taker was made
in November 1958. The main requirement was a programming system for
manipulating expressions representing formalized declarative and imperative
sentences so that the Advice Taker system could make deductions.
In the course of its development the LISP system went through several
stages of simplification and event ually came to be based on a scheme for rep-
resenting the partial recursive functions of a certain class of symbolic expres-
sions. This representation is independent of the IBM 704 computer, or of any
other electronic computer, and it now seems expedient to expound the system
by starting with the class of expressions called S-expressions and the functions
called S-functions.
∗
Putting this paper in L
A
T
E
Xpartly s upported by ARPA (ONR) grant N00014 -94-1-07 75
to Stanford University where John McCarthy has been since 1962. Copied with minor nota-
tional changes from CACM, April 1960. If you want the exact typography, look there. Cur-
rent a ddress, John McCa rthy, Computer Science Department, Stanford, CA 94305, (email:
jmc@cs.stanford.edu), (URL: http://www-formal.stanford.edu/jmc/ )
1
In this a rt icle, we first describe a formalism for defining functions recur-
sively. We believe this formalism has advantages bo th as a programming
language and as a vehicle f or developing a theory of computation. Next, we
describe S-expressions and S-functions, give some examples, and then describe
the universal S-function apply which plays the theoretical role of a universal
Turing machine and the practical role of an interpreter. Then we describe the
representation of S-expressions in the memory of the IBM 704 by list structures
similar to those used by Newell, Shaw and Simon [2], and the representation
of S-functions by program. Then we mention t he main features of the LISP
programming system for the IBM 704. Next comes another way of describ-
ing computations with symbolic expressions, and finally we give a recursive
function interpretation of flow charts.
We hope to describe some of the symbolic computations for which LISP
has been used in another paper, and also to give elsewhere some applications
of our recursive function formalism to mathematical logic and to the problem
of mechanical theorem proving.
2 Functions and Function Definitions
We shall need a number of mathematical ideas and notations concerning func-
tions in general. Most of the ideas a r e well known, but the notion of conditional
expression is believed to b e new
1
, and the use of conditional expressions per-
mits functions to be defined recursively in a new and convenient way.
a. Partial Functions. A partial function is a function that is defined only
on part of its domain. Partial functions necessarily arise when functions are
defined by computations because for some values of the arguments the com-
putation defining the value of the function may not terminate. However, some
of our elementary functions will be defined as partial functions.
b. Propositional Expressions and Predicates. A propositional expression is
an expressio n whose possible values are T (for truth) and F (for falsity). We
shall assume that the reader is familiar with the propositional connectives ∧
(“and”), ∨ (“or”), and ¬ (“not”). Typical propositional expressions are:
1
reference Kleene
2
x < y
(x < y) ∧ (b = c)
x is prime
A predicate is a function whose range consists of the truth values T and F.
c. Conditional Expressions. The dependence of truth values on the values
of quantities of other kinds is expressed in mathematics by predicates, and the
dependence of truth values on other truth values by logical connectives. How-
ever, the notations for expressing symbolically the dependence of quantities of
other kinds on truth values is inadequate, so that English words and phrases
are generally used for expressing these dependences in texts that describe other
dependences symbolically. For example, the function |x| is usually defined in
words. Conditional expressions are a device for expressing the dependence of
quantities on propositional quantities. A conditional expressio n has the form
(p
1
→ e
1
, · · · , p
n
→ e
n
)
where the p’s are propositional expressions and the e’s are expressions of any
kind. It may be r ead, “If p
1
then e
1
otherwise if p
2
then e
2
, · · · , otherwise if
p
n
then e
n
,” or “p
1
yields e
1
, · · · , p
n
yields e
n
.”
2
We now give the r ules for determining whether the value of
(p
1
→ e
1
, · · · , p
n
→ e
n
)
is defined, and if so what its value is. Examine the p’s from left to right. If
a p whose value is T is encounter ed before a ny p whose value is undefined is
encountered then the value of the conditional expression is the value of the
corresponding e (if this is defined). If any undefined p is encountered before
2
I sent a proposal fo r conditional expressions to a CACM forum on what should be
included in Algol 60. Because the item was sho rt, the editor demoted it to a letter to the
editor, fo r which CACM subsequently apologized. The notation given here was rejected for
Algol 60, because it had been decided that no new mathematical notation should be allowed
in Algol 60, and everything new had to be English. The if . . . then . . . else that Algol 60
adopted was suggested by John Backus .
3
a t r ue p, or if all p’s are false, or if the e corresponding to the first true p is
undefined, then the value of the conditional expression is undefined. We now
give examples.
(1 < 2 → 4, 1 > 2 → 3) = 4
(2 < 1 → 4, 2 > 1 → 3, 2 > 1 → 2) = 3
(2 < 1 → 4, T → 3) = 3
(2 < 1 →
0
0
, T → 3 ) = 3
(2 < 1 → 3, T →
0
0
) is undefined
(2 < 1 → 3, 4 < 1 → 4) is undefined
Some of the simplest applications of conditional expressions are in giving
such definitions as
|x| = (x < 0 → −x, T → x)
δ
ij
= (i = j → 1, T → 0)
sgn(x) = (x < 0 → −1, x = 0 → 0, T → 1)
d. Rec ursive Function Definitions. By using conditional expressions we
can, without circularity, define functions by formulas in which the defined
function occurs. For example, we write
n! = (n = 0 → 1, T → n · (n − 1)!)
When we use this formula to evaluate 0! we get the answer 1 ; because of the
way in which the value of a conditional expression was defined, the meaningless
4
expression 0 · (0 - 1)! does not ar ise. The evaluation of 2! according to this
definition proceeds as follows:
2! = (2 = 0 → 1, T → 2 · (2 − 1)!)
= 2 · 1!
= 2 · (1 = 0 → 1T → ·(1 − 1)!)
= 2 · 1 · 0!
= 2 · 1 · (0 = 0 → 1, T → 0 · (0 − 1)!)
= 2 · 1 · 1
= 2
We now give two other applications of recursive function definitions. The
greatest common divisor, gcd(m,n), of two positive integers m and n is com-
puted by means of the Euclidean algorithm. This algorithm is expressed by
the recursive function definition:
gcd(m, n) = (m > n → gcd(n, m), rem(n, m) = 0 → m, T → gcd(rem(n, m), m))
where rem(n, m) denotes the remainder left when n is divided by m.
The Newtonian algorithm for obta ining an approximate square root of a
number a, starting with an initial approximation x and requiring that an
acceptable approximation y satisfy |y
2
− a| < ǫ, may be written as
sqrt(a, x, ǫ)
= (|x
2
− a| < ǫ → x,T → sqrt (a,
1
2
(x +
a
x
), ǫ))
The simultaneous recursive definition of several functions is also possible,
and we shall use such definitions if they are required.
There is no guarantee that the computation determined by a recursive
definition will ever terminate and, for example, an attempt to compute n!
from our definition will only succeed if n is a non-negative integer. If the
computation does not terminate, the function must be regarded as undefined
for the given arguments.
The propositional connectives themselves can be defined by conditional
expressions. We write
5
p ∧ q = (p → q, T → F )
p ∨ q = (p → T, T → q)
¬p = (p → F, T → T )
p ⊃ q = (p → q, T → T )
It is readily seen that the right-hand sides of the equations have the correct
truth tables. If we consider situations in which p or q may be undefined, the
connectives ∧ and ∨ are seen to be noncommutative. For example if p is false
and q is undefined, we see that according to the definitions given above p ∧ q
is false, but q ∧ p is undefined. For our applications this noncommutativity is
desirable, since p ∧q is computed by first computing p, and if p is false q is not
computed. If the computation for p does not terminate, we never get around
to computing q. We shall use propositional connectives in this sense hereafter.
e. Function s and Forms. It is usual in mathematics—outside of mathe-
matical logic—to use the word “function” imprecisely and to apply it to fo rms
such as y
2
+ x. Because we shall later compute with expressions for functions,
we need a distinction between functions and forms and a notation for express-
ing this distinction. This distinction and a notation for describing it, from
which we deviate trivially, is given by Church [3].
Let f be an expression that stands for a function of two integer variables.
It should make sense to write f (3, 4) and the value of this expression should be
determined. The expression y
2
+x does not meet this requirement; y
2
+x(3, 4)
is not a conventional notation, and if we attempted to define it we would be
uncertain whether its value would turn out to be 13 or 19. Church calls an
expression like y
2
+ x, a form. A form can be converted into a function if we
can determine the correspondence between the variables occurring in the form
and the ordered list of arguments of the desired function. This is accomplished
by Church’s λ-nota t io n.
If E is a form in variables x
1
, · · · , x
n
, then λ((x
1
, · · · , x
n
), E) will be taken
to be the f unction of n variables whose value is determined by substituting
the arguments for the variables x
1
, · · · , x
n
in that order in E and evaluating
the resulting expression. For example, λ((x, y), y
2
+ x) is a function of two
variables, and λ((x, y), y
2
+ x)(3, 4) = 19.
The variables occurring in the list of variables of a λ-expression are dummy
or bound, like variables of integration in a definite integral. That is, we may
6
change the names of the bound variables in a function expression without
changing the value of the expression, provided that we make the same change
for each occurrence of the variable and do not make two variables the same
that previously were different . Thus λ((x, y), y
2
+ x), λ((u, v), v
2
+ u) and
λ((y, x), x
2
+ y) denote the same function.
We shall frequently use expressions in which some of the variables are
bound by λ’s and others are not. Such an expression may be regarded as
defining a function with parameters. The unbound variables are called free
variables.
An adequate notatio n that distinguishes functions from forms allows an
unambiguous treatment of functions of functions. It would involve too much
of a digression to give examples here, but we shall use f unctions with functions
as ar guments la ter in this report.
Difficulties arise in combining functions described by λ-expressions, or by
any other notation involving variables, b ecause different bound variables may
be represented by the same symbol. This is called collision of bound variables.
There is a notatio n involving operators that are called combinators fo r com-
bining functions without the use of va r ia bles. Unfortunately, the combinatory
expressions for interesting combinations of functions tend to be lengthy a nd
unreadable.
f. Expressions for Recursive Functions. The λ-notation is inadequate for
naming functions defined recursively. For example, using λ’s, we can convert
the definition
sqrt(a, x, ǫ) = (|x
2
− a| < ǫ → x, T → sqrt(a,
1
2
(x +
a
x
), ǫ))
into
sqrt = λ((a, x, ǫ), (|x
2
− a| < ǫ → x, T → sqrt(a,
1
2
(x +
a
x
), ǫ))),
but t he right-hand side cannot serve as an expression for the function be-
cause there would be nothing to indicate that the reference to sqrt within the
expression stood for the expression as a whole.
In order to be a ble to write expressions for recursive functions, we intro-
duce another notation. label(a, E) denotes the expression E, provided that
occurrences of a within E are to be interpreted as referring to the expression
7
as a whole. Thus we can write
label(sqrt, λ((a, x, ǫ), (|x
2
− a| < ǫ → x, T → sqrt(a,
1
2
(x +
a
x
), ǫ))))
as a name for our sqrt function.
The symbol a in label (a, E) is also bound, that is, it may be altered
systematically without changing the meaning of the expression. It behaves
differently from a variable bound by a λ, however.
3 Recursive Functions of Symbolic Expr essions
We shall first define a class of symbolic expressions in terms of ordered pairs
and lists. Then we shall define five elementary functions and predicates, and
build from them by composition, conditional expressions, and recursive def-
initions an extensive class of functions o f which we shall give a number of
examples. We shall then show how these functions themselves can be ex-
pressed as symbolic expressions, and we shall define a universal function apply
that allows us to compute from the expression for a given function its value
for given arguments. F inally, we shall define some functions with functions as
arguments and give some useful examples.
a. A Class of Symbolic Expressions. We shall now define the S-expressions
(S stands for symbolic). They are formed by using the special characters
·
)
(
and an infinite set of distinguishable atomic symbols. For atomic symbols,
we shall use strings of capital Latin letters and digits with single imbedded
8
blanks.
3
Examples of atomic symbols are
A
ABA
AP P LE P IE NUMBER 3
There is a twofold reason for departing from the usual mathematical prac-
tice of using single letters for atomic symbols. First, computer programs fre-
quently require hundreds of distinguishable symbols that must be formed from
the 47 characters that are printable by the IBM 70 4 computer. Second, it is
convenient to allow English words and phrases to stand for atomic entities for
mnemonic reasons. The symbols are atomic in the sense that any substructure
they may have as sequences of characters is ignored. We assume o nly that dif-
ferent symbols can be distinguished. S-expressions are then defined as follows:
1. Atomic symbols are S-expressions.
2. If e
1
and e
2
are S-expressions, so is (e
1
· e
2
).
Examples of S-expressions ar e
AB
(A · B)
((AB · C) · D)
An S-expression is then simply an ordered pair, the terms of which may be
atomic symbols or simpler S-expressions. We can can represent a list of arbi-
trary length in terms of S-expressions as follows. The list
(m
1
, m
2
, · · · , m
n
)
is represented by the S-expression
(m
1
· (m
2
· (· · · (m
n
· NIL) · · ·)))
Here NIL is an atomic symbol used to terminate lists. Since many of the
symbolic expressions with which we deal are conveniently expressed as lists,
we shall introduce a list notation to abbreviate certain S-expressions. We have
3
1995 remark: Imbedded blanks could be allowed w ithin symbols, be c ause lists were then
written with commas between elements.
9
l. (m) stands for (m ·NIL).
2. (m
1
, · · · , m
n
) stands for (m
1
· (· · · (m
n
· NIL) · · ·)).
3. (m
1
, · · · , m
n
· x) stands for (m
1
· (· · · (m
n
· x) · · ·)).
Subexpressions can be similarly abbreviated. Some examples of these ab-
breviations are
((AB, C), D) for ((AB · (C · NIL)) · (D · NIL))
((A, B), C, D · E) for ((A · (B · NIL)) · (C · (D · E)))
Since we regard the expressions with commas as abbreviations f or those
not involving commas, we shall refer to them all as S-expressions.
b. Functions of S-expre s sions and the Expressions That Represent Them.
We now define a class of functions of S-expressions. The expressions represent-
ing these functions are written in a conventional functional notation. However,
in order to clearly distinguish the expressio ns representing functions from S-
expressions, we shall use sequences of lower-case letters for function names
and variables ranging over the set of S-expressions. We also use brackets and
semicolons, instead of parentheses and commas, fo r denoting the application
of functions to their arguments. Thus we write
car[x]
car[cons[(A · B); x]]
In these M- expressions (meta-expressions) any S-expression that occur stand
for themselves.
c. The Elementary S-functions and Predicates. We introduce the following
functions and predicates:
1. ato m. atom[x] has t he value of T or F according to whether x is an
atomic symbol. Thus
atom [X] = T
atom [(X · A)] = F
2. eq. eq [x;y] is defined if and only if both x and y a r e atomic. eq [x; y]
= T if x and y are the same symbol, and eq [x; y] = F otherwise. Thus
10
eq [X; X] = T
eq [X; A] = F
eq [X; (X · A)] is undefined.
3. car. car[x] is defined if and only if x is not atomic. car [(e
1
· e
2
)] = e
1
.
Thus car [X] is undefined.
car [(X · A)] = X
car [((X · A) · Y )] = (X · A)
4. cdr. cdr [x] is also defined when x is not atomic. We have cdr
[(e
1
· e
2
)] = e
2
. Thus cdr [X] is undefined.
cdr [(X · A)] = A cdr [((X · A) · Y )] = Y
5. cons. cons [x; y] is defined for any x and y. We have cons [e
1
; e
2
] =
(e
1
· e
2
). Thus
cons [X; A] = (X A)
cons [(X · A); Y ] = ((X · A)Y )
car, cdr, and cons are easily seen to satisfy the relations
car [cons [x; y]] = x
cdr [cons [x; y]] = y
cons [car [x]; cdr [x]] = x, provided that x is not atomic.
The names “car” and “cons” will come to have mnemonic significance only
when we discuss the representation of the system in the computer. Composi-
tions of car and cdr give the subexpressio ns of a given expression in a given
position. Compo sitions of cons form expressions of a given structure out of
parts. The class of functions which can be formed in this way is quite limited
and not very interesting.
d. Recursive S-functions. We get a much larger class of functions (in fact,
all computable functions) when we allow ourselves to form new functions of
S-expressions by conditional expressions and recursive definition. We now give
11
some examples of functions that are definable in this way.
1. ff[x]. The value of ff[x] is the first atomic symbol of the S-expression x
with the parentheses ignored. Thus
ff[((A · B) · C)] = A
We have
ff[x] = [atom[x] → x; T → ff[car[x]]]
We now trace in detail the steps in the evaluation of
ff [((A · B) · C)]:
ff [((A · B) · C)]
= [atom[((A · B) · C)] → ((A · B) · C); T → ff[car[((A · B)C·)]]]
= [F → ((A · B) · C); T → ff[car[((A · B) · C)]]]
= [T → ff[car[((A · B) · C)]]]
= ff[car[((A · B) · C)]]
= ff[(A · B)]
= [atom[(A · B)] → (A · B); T → ff[car[(A · B)]]]
= [F → (A · B); T → ff[car[(A · B)]]]
= [T → ff[car[(A · B)]]]
= ff[car[(A · B)]]
= ff[A]
12
= [atom[A] → A; T → ff[car[A]]]
= [T → A; T → ff[car[A]]]
= A
2. subst [x; y; z]. This function gives the result of substituting the S-
expression x for all occurrences of the atomic symbol y in the S-expression z.
It is defined by
subst [x; y; z] = [atom [z] → [eq [z; y] → x; T → z];
T → cons [subst [x; y; car [z]]; subst [x; y; cdr [z]]]]
As an example, we have
subst[(X · A); B; ((A · B) · C)] = ((A · (X · A)) · C)
3. equal [x; y]. This is a predicate that has the value T if x and y are the
same S-expression, and has the value F otherwise. We have
equal [x; y] = [atom [x] ∧ atom [y] ∧ eq [x; y]]
∨[¬ atom [x] ∧¬ atom [y] ∧ equal [car [x]; car [y]]
∧ equal [cdr [x]; cdr [y]]]
It is convenient to see how the elementary functions look in the abbreviated
list notation. The reader will easily verify that
(i) car[(m
1
, m
2
, · · · , m
n
)] = m
1
(ii) cdr[(m
s
, m
2
, · · · , m
n
)] = (m
2
, · · · , m
n
)
(iii) cdr[(m)] = NIL
(iv) cons[m
1
; (m
2
, · · · , m
n
)] = (m
1
, m
2
, · · · , m
n
)
(v) cons[m; NIL] = (m)
We define
13
null[x] = atom[x] ∧ eq[x; NIL]
This predicate is useful in dealing with lists.
Compositions of car and cdr arise so frequently that many expressions can
be written more concisely if we abbreviate
cadr[x] for car[cdr[x]],
caddr[x] fo r car[cdr[cdr[x]]], etc.
Another useful abbreviation is to write list [e
1
; e
2
; · · · ; e
n
]
for cons[e
1
; cons[e
2
; · · · ; cons[e
n
; NIL] · · ·]].
This function gives the list, (e
1
, · · · , e
n
), as a f unction of its elements.
The following functions are useful when S-expressions are regarded as lists.
1. append [x;y].
append [x; y] = [null[x] → y; T → cons [car [x]; append [cdr [x]; y]]]
An example is
append [(A, B); (C, D, E)] = (A, B, C, D, E)
2. among [x;y]. This predicate is true if the S-expression x occurs among
the elements of the list y. We have
among[x; y] = ¬null[y] ∧ [equal[x; car[y]] ∨ among[x; cdr[y]]]
3. pair [x;y]. This function gives t he list of pairs of corresponding elements
of the lists x and y. We have
pair[x; y] = [null[x]∧null[y] → NIL; ¬atom[x]∧¬atom[y] → cons[list[car[x]; car[y]]; pair[cdr[x]; cdr[y]]]
An example is
pair[(A, B, C); (X, (Y, Z), U)] = ((A, X), (B, (Y, Z)), (C, U)).
14
4. assoc [x;y]. If y is a list of the form ((u
1
, v
1
), · · · , (u
n
, v
n
)) and x is one
of the u’s, then assoc [x; y] is the corresponding v. We have
assoc[x; y] = eq[caar[y]; x] → cadar[y]; T → assoc[x; cdr[y]]]
An example is
assoc[X; ((W, (A, B)), (X, (C, D)), (Y, (E, F )))] = (C, D).
5. sublis[x; y]. Here x is a ssumed to have the for m of a list of pairs
((u
1
, v
1
), · · · , (u
n
, v
n
)), where the u’s are atomic, and y may be any S-expression.
The va lue of sublis[x; y] is the result of substituting each v for the correspond-
ing u in y. In order to define sublis, we first define an auxiliary function. We
have
sub2[x; z] = [null[x] → z; eq[caar[x]; z] → cadar[x]; T → sub2[cdr[x]; z]]
and
sublis[x; y] = [atom[y] → sub2[x; y]; T → cons[sublis[x; car[y]]; sublis[x; cdr[y]]]
We have
sublis [((X, (A, B)), (Y, (B, C))); (A, X · Y)] = (A, (A, B), B, C)
e. Representation of S-Func tion s by S-Expressions. S- functions have been
described by M-expressions. We now give a rule for translating M-expressio ns
into S-expressions, in order to be able to use S-functions for making certain
computations with S-functions and for answering certain questions about S-
functions.
The t r anslation is determined by the following rules in rich we denote the
translation o f an M-expression E by E*.
1. If E is an S- expression E* is (QUOTE, E).
2. Variables and function names that were represented by strings of lower-
case letters are translated to the corresponding strings of the corresponding
uppercase letters. Thus car* is CAR, and subst* is SUBST.
3. A form f [e
1
; · · · ; e
n
] is translated to (f
∗
, e
∗
1
· · · , e
∗
n
). Thus cons [car [x];
cdr [x]]
∗
is (CONS, (CAR, X), (CDR, X)).
4. {[p
1
→ e
1
; · · · ; p
n
→ e
n
]}
∗
is (COND, (p
∗
1
, e
∗
1
), · · · , (p
∗
n
· e
∗
n
)).
15
5. {λ[[x
1
; · · · ; x
n
]; E]}
∗
is (LAMBDA, (x
∗
1
, · · · , x
∗
n
), E
∗
).
6. {label[a; E]}
∗
is (LABEL, a
∗
, E
∗
).
With t hese conventions the substitution function whose M-expression is
label [subst; λ [[x; y; z]; [atom [z] → [eq [y; z] → x; T → z]; T → cons [subst
[x; y; car [z]]; subst [x; y; cdr [z]]]]]] has the S-expression
(LABEL, SUBST, (LAMBDA, (X, Y, Z), (COND ((ATOM, Z), (COND,
(EQ, Y, Z), X), ((QUOTE, T), Z))), ((QUOTE, T), (CONS, (SUBST, X, Y,
(CAR Z ) ), (SUBST, X, Y, (CDR, Z)))))))
This not ation is writable and somewhat readable. It can be made easier
to read and write at the cost of making its structure less regular. If more
characters were available on t he computer, it could be improved considerably.
4
f. The Universa l S-Function apply. There is an S-function apply with the
property that if f is an S-expression for an S-function f
′
and args is a list of
arguments of the form (arg
1
, · · · , arg
n
), where arg
1
, · · · , arg
n
are arbitrary S-
expressions, then apply[f ; args] and f
′
[arg
1
; · · · ; arg
n
] are defined for the same
values of arg
1
, · · · , arg
n
, and are equal when defined. For example,
λ[[x; y]; cons[car[x]; y]][(A, B) ; (C, D)]
= apply[(LAMBDA, (X, Y ), (CONS, (CAR, X), Y )); ((A, B), (C, D))] = (A, C, D)
The S-function apply is defined by
apply[f ; args] = eval[cons[f; appq[args]]; NIL],
where
appq[m] = [null[m] → NIL; T → cons[list[QUOT E; car[m]]; appq[cdr[m]]]]
and
eval[e; a] = [
4
1995: More characters were made available on SAIL and later on the Lisp machines.
Alas, the world went back to inferior character sets again—though not as far back as when
this paper was written in early 1959.
16
atom [e] → a ssoc [e; a];
atom [car [e]] → [
eq [car [e]; QUOTE] → cadr [e];
eq [car [e]; ATOM] → atom [eval [cadr [e]; a]];
eq [car [e]; EQ] → [eval [cadr [e]; a] = eval [caddr [e]; a]];
eq [car [e]; COND] → evcon [cdr [e]; a];
eq [car [e]; CAR] → car [eval [cadr [e]; a]];
eq [car [e]; CDR] → cdr [eval [cadr [e]; a]];
eq [car [e]; CONS] → cons [eval [cadr [e]; a]; eval [caddr [e];
a]]; T → eval [cons [assoc [car [e]; a];
evlis [cdr [e]; a]]; a]];
eq [caar [e]; LABEL] → eval [cons [caddar [e]; cdr [e]];
cons [list [cadar [e]; car [e]; a]];
eq [caar [e]; LAMBDA] → eval [caddar [e];
append [pair [cadar [e]; evlis [cdr [e]; a]; a]]]
and
evcon[c; a] = [eval[caar[c]; a] → eval[cadar[c]; a]; T → evcon[cdr[c]; a]]
and
evlis[m; a] = [null[m] → NIL; T → cons[eval[car[m]; a]; evlis[cdr[m]; a]]]
17
We now explain a number of points about these definitions.
5
1. apply itself forms an expression representing the value of the function
applied to the arguments, and puts the work of evaluating this expression onto
a function eval. It uses appq to put quotes around each of the arguments, so
that eval will regard them as standing for themselves.
2. eval[e; a] has two arguments, an expression e to be evaluated, and a list
of pairs a. The first item of each pair is an a t omic symbol, and the second is
the expression for which the symbol stands.
3. If the expression to be evaluated is atomic, eval evaluates whatever is
paired with it first on the list a.
4. If e is not at omic but car[e] is atomic, then the expression has one of the
forms (QUOT E, e) or (AT OM, e) or (EQ, e
1
, e
2
) or (CON D, (p
1
, e
1
), · · · , (p
n
, e
n
)),
or (CAR, e) or (CDR, e) or (CONS, e
1
, e
2
) or (f, e
1
, · · · , e
n
) where f is an
atomic symbol.
In the case (QUOT E, e) the expression e, itself, is taken. In the case of
(AT OM, e) or (CAR, e) or (CDR, e) the expression e is evaluated and the
appropriate function taken. In the case of (EQ, e
1
, e
2
) or (CON S, e
1
, e
2
) two
expressions have to be evaluated. In the case of (COND, (p
1
, e
1
), · · · (p
n
, e
n
))
the p’s have to be evaluated in order until a true p is found, and then the
corresponding e must be evaluated. This is accomplished by evcon. Finally, in
the case o f (f, e
1
, · · · , e
n
) we evaluate the expression that results from replacing
f in this expression by whatever it is paired with in the list a.
5. The evaluation of ((LABEL, f, E), e
1
, · · · , e
n
) is accomplished by eval-
uating (E, e
1
, · · · , e
n
) with the pairing (f, (LABEL, f, E)) put on the f r ont of
the previous list a of pairs.
6. Finally, the evaluation of ((LAMBDA, (x
1
, · · · , x
n
), E), e
1
, · · · e
n
) is ac-
complished by evaluating E with the list of pairs ((x
1
, e
1
), · · · , ((x
n
, e
n
)) put
on the front of the previous list a.
The list a could be eliminated, and LAMBDA and LABEL expressions
evaluated by substituting the arguments for the variables in the expressions
E. Unfortunately, difficulties involving collisions of bound variables arise, but
they are avoided by using the list a.
5
1995: This version isn’t quite right. A compa rison of this and other versions of eval
including what was actually implemented (and debugged) is given in “The Influence of the
Designer on the Design” by Herbert Stoyan and included in Artificial Intelligence and Math-
ematical Theory of Computation: Papers in Honor of John McCarthy, Vladimir Lifschitz
(ed.), Academic Press, 1991
18
Calculating the values of functions by using apply is an activity better
suited to electronic computers than to people. As an illustration, however, we
now give some of the steps for calculating
apply [(LABEL, FF, (LAMBDA, (X), (COND, (ATOM, X), X), ((QUOTE,
T),(FF, (CAR, X))))));((A· B))] = A
The first argument is the S-expression that represents the f unction ff defined
in section 3d. We shall abbreviate it by using the letter φ. We have
apply [φ; ( (A·B) )]
= eval [((LABEL, FF, ψ), (QUOTE, (A·B))); NIL]
where ψ is the part of φ beginning (LAMBDA
= eval[((LAMBDA, (X), ω), (QUOTE, (A·B)));((FF, φ))]
where ω is the part of ψ beginning (COND
= eval [(COND, (π
1
, ǫ
1
), (π
2
, ǫ
2
)); ((X, (QUOTE, (A·B) ) ), (FF, φ) )]
Denoting ((X, (QUOTE, (A·B))), (FF, φ)) by a, we obtain
= evcon [((π
1
, ǫ
1
), (π
2
, ǫ
2
)); a]
This involves eval [π
1
; a]
= eval [( ATOM, X); a]
= atom [eval [X; a]]
= atom [eval [assoc [X; ((X, (QUOTE, (A·B))), (FF,φ) )];a]]
= atom [eval [(QUOTE, ( A·B)); a]]
= atom [(A·B)],
= F
Our main calulation continues with
19
apply [φ; ((A·B))]
= evcon [((π
2
, ǫ
2
, )); a],
which invo lves eval [π
2
; a] = eval [(QUOTE, T); a] = T
Our main calculation again continues with
apply [φ; ((A·B))]
= eval [ǫ
2
; a]
= eval [(FF, (CAR, X));a]
= eval [Cons [φ; evlis [((CAR, X)); a]]; a]
Evaluating evlis [((CAR, X)); a] involves
eval [(CAR, X); a]
= car [eval [X; a]]
= car [(A·B)], where we took steps from the earlier computation of atom [eval [X; a]] = A,
and so evlis [((CAR, X)); a] then becomes
list [list [QUOTE; A]] = ((QUOTE, A)),
and o ur main quantity becomes
= eval [(φ, (QUOTE, A)); a]
The subsequent steps are made as in the beginning of the calculation. The
LABEL and L AMBDA cause new pairs to be added to a, which gives a new
list of pairs a
1
. The π
1
term of the conditional eva l [(ATOM, X); a
1
] has the
20
value T because X is paired with (QUOTE, A) first in a
1
, rather than with
(QUOTE, (A·B)) as in a.
Therefore we end up with eval [X; a
1
] f r om the evcon, and this is j ust A.
g. Functions with Functions as Arguments. There are a number of useful
functions some of whose arguments are functions. They are especially useful
in defining other functions. One such function is maplist[x; f ] with an S-
expression argument x and an argument f that is a function from S-expressions
to S-expressions. We define
maplist[x; f] = [null[x] → NIL; T → cons[f[x]; maplist[cdr[x]; f]]]
The usefulness of maplist is illustrated by formulas fo r the partial derivative
with respect to x of expressions involving sums and products of x and other
variables. The S-expressions that we shall differentiate are formed as follows.
1. An atomic symbol is an allowed expression.
2. If e
1
, e
2
, · · · , e
n
are allowed expressions, ( PLUS, e
1
, · · · , e
n
) and (TIMES,
e
1
, · · · , e
n
) are also, and represent the sum and product, respectively, of e
1
, · · · , e
n
.
This is, essentially, the Polish notation for functions, except that the in-
clusion of parentheses and commas allows functions of variable numbers of
arguments. An example of an allowed expression is (TIMES, X, (PLUS, X,
A), Y), the conventional algebraic notation for which is X(X + A)Y.
Our differentiation formula, which gives the derivative of y with respect to
x, is
diff [y; x] = [atom [y] → [eq [y; x] → ONE; T → ZERO]; eq [car [Y]; PLUS]
→ cons [PLUS; maplist [cdr [y]; λ[[z]; diff [car [z]; x]]]]; eq[car [y]; TIMES] →
cons[PLUS; maplist[cdr[y]; λ[[z]; cons [TIMES; maplist[cdr [y]; λ[[w]; ¬ eq [z;
w] → car [w]; T → diff [car [[w]; x]]]]]]]
The derivative of the expression (TIMES, X, (PLUS, X, A), Y), as com-
puted by this f ormula, is
(PLUS, (TIMES, ONE, (PLUS, X, A), Y), (TIMES, X, (PLUS, ONE,
ZERO), Y), (TIMES, X, (PLUS, X, A), ZERO))
Besides maplist, another useful function with functional a r guments is search,
which is defined as
search[x; p; f ; u] = [null[x] → u; p[x] → f[x]; T → search[cdr[x]; p; f; u]
21
The function search is used to search a list for an element that has the property
p, and if such an element is found, f of that element is taken. If there is no
such element, the function u of no arguments is computed.
4 The LISP Programming System
The LISP programming system is a system for using the IBM 704 computer to
compute with symbolic information in the form o f S-expressions. It has b een
or will be used for the following purpo ses:
l. Writing a compiler to compile LISP programs into machine language.
2. Writing a program to check proofs in a class of formal logical systems.
3. Writing programs for formal differentiation and integration.
4. Wr iting programs to realize various algorithms f or generating proo fs in
predicate calculus.
5. Making certain engineering calculations whose results are formulas
rather than numbers.
6. Programming the Advice Taker system.
The basis of the system is a way of writing computer programs to evaluate
S-functions. This will be described in the following sections.
In addition to the facilities for describing S-functions, there are facilities
for using S-functions in programs written as sequences of statements along the
lines of FORTRAN (4) or ALGO L (5). These features will not be describ ed
in this article.
a. Representation of S-Expressions by List Structure. A list structure is a
collection of computer words arranged as in figure 1a or 1b. Each word of the
list structure is represented by one of the subdivided rectangles in the figure.
The left box of a rectangle represents the address field of the word and the
right box represents the decrement field. An arrow from a box t o anot her
rectangle means that the field corresponding to the box contains the location
of the word corresponding to the other rectangle.
22
✲ ✲
✲
✲ ✲
✲
✲
✲
✲
✲
✲
✲ ✲ ✲
✲
✲
✲ ✲
✲
✲
✲
✲
✲
Fig. 1
It is permitted for a substructure to occur in more than one place in a list
structure, as in figure 1b, but it is not permitted for a structure to have cycles,
as in figure 1c. An atomic symbol is represented in the computer by a list
structure of special form called the association list of the symbol. The address
field of the first word contains a special constant which enables the program to
tell that this wo rd represents an atomic symbol. We shall describe association
lists in section 4b.
An S-expression x that is not atomic is represented by a word, the address
and decrement parts of which contain the locations of the subexpressions car[x]
and cdr[x], respectively. If we use t he symbols A, B, etc. to denote the
locations of the association list of these symbols, then the S-expression ((A ·
B) · (C · (E · F ))) is represented by the list structure a of figure 2. Turning
to the list fo r m of S-expressions, we see that the S-expression (A, (B, C), D),
which is an abbreviation for (A · ((B · (C · NIL)) · (D · NIL))), is represented
by the list structure of figure 2b.
23
✲
✲
✲
✲
✲ ✲ ✲
✲ ✲
A B
E F
C
A
B C
D
(a)
(b)
Figure 2
When a list structure is regarded as representing a list, we see that each term
of the list occupies the address part of a word, the decrement part of which
points to the word containing the next term, while the last word has NIL in
its decrement.
An expression that has a given subexpression occurring more than once
can be represented in more than one way. Whether the list structure for
the subexpression is or is not r epeated depends upon t he history of the pro-
gram. Whether or not a subexpression is repeated will make no difference
in the results of a program as t hey appear outside the machine, although it
will affect the time a nd storage requirements. Fo r example, the S- expression
((A·B)·(A·B)) can be represented by either the list structure of figure 3a or
3b.
✲
✲ ✲
✲
✲
✲
A B A B
A B
(a)
(b)
Figure 3
The prohibition against circular list structures is essentially a prohibition
24
against an expression being a subexpression of itself. Such an expression could
not exist on paper in a world with o ur to polog y. Circular list structures would
have some advantages in the machine, for example, for representing recursive
functions, but difficulties in printing them, and in certain other operations,
make it seem advisable not to use them for the present.
The advant ages of list structures for the storage of symbolic expressions
are:
1. The size and even the number of expressions with which the program
will have to deal cannot be predicted in advance. Therefore, it is difficult to
arrange blocks of storage of fixed length to cont ain them.
2. Registers can be put back on the free-storage list when they are no longer
needed. Even one register returned to the list is of value, but if expressions
are stored linearly, it is difficult to make use of blocks of registers of odd sizes
that may become available.
3. An expression that occurs as a subexpression of several expressions need
be represented in storage only once.
b. Associ ation Lis ts
6
. In the LISP programming system we put more in
the association list of a symbol than is required by the mathematical system
described in the previous sections. In fact, any information that we desire to
associate with the symbol may be put on the association list. This information
may include: the print name, t hat is, the string of letters and digits which
represents the symbol outside the machine; a numerical value if the symbol
represents a number; another S-expressio n if the symbol, in some way, serves
as a name for it; or the location of a routine if the symb ol represents a function
for which there is a machine-language subroutine. All this implies that in the
machine system there are more primitive entities than have been described in
the sections on the mathematical system.
For the present, we shall only describe how print names are represented
on association lists so that in reading or printing the program can establish
a correspondence between information on punched cards, magnetic tape or
printed page and the list structure inside the machine. The association list of
the symbol DIFFERENTIATE has a segment of the form shown in figure 4.
Here pname is a symbol that indicates that the structure for the print name
of the symbol whose association list this is hanging from the next word on
the association list. In the second row of the figure we have a list of three
words. The address part of each of these words points to a Word containing
6
1995: These were later called property lists.
25
six 6-bit characters. The last word is filled out with a 6-bit combination that
does not represent a character printable by the computer. (Recall that the
IBM 7O4 has a 36-bit word and that printable characters are each represented
by 6 bits.) The presence of the words with character information means that
the association lists do not themselves represent S-expressions, and that only
some of the functions for dealing with S-expressions make sense within an
association list.
✲
✲ ✲
✲ ✲ ✲
✲ ✲
✲
pname
...
....
DIFFER ENTIAT E ??????
Figure 4
c. Free-Storage List. At any given time only a part of the memory reserved
for list structures will actually be in use for storing S- expressions. The remain-
ing registers (in our system the number, initially, is approximately 15,000) are
arranged in a single list called the free-storage list. A certain register, FREE,
in the program contains the location of the first register in this list. When
a word is required to form some additional list structure, the first word on
the free-storage list is taken and the number in register FREE is chang ed to
become the location of the second word on the free-storage list. No provision
need be made for the user to program the return of registers to the free-storage
list.
This return takes place automatically, approximately as follows (it is nec-
essary to give a simplified description of this process in this report): There is
a fixed set of base registers in the program which contains the locations of list
structures that are accessible to the program. Of course, because list struc-
tures branch, a n a rbitra r y number of r egisters may be involved. Each register
that is accessible to the program is accessible because it can be reached from
one or more of the base registers by a chain of car and cdr operations. When
26
the contents of a base register are changed, it may happen that the register
to which the base register formerly pointed cannot be reached by a car − cdr
chain from any base register. Such a register may be considered abandoned
by the program because its contents can no longer be found by any possible
program; hence its contents are no longer of interest, and so we would like to
have it back on the free-storage list. This comes about in the following way.
Nothing happens until the program runs out of free storage. When a free
register is wanted, and t here is none left on the free-storage list, a reclamation
7
cycle starts.
First, the program finds all registers accessible from the base registers and
makes their signs negative. This is accomplished by starting from each of the
base registers and changing the sign of every register that can be reached from
it by a car − cdr chain. If the program encounters a register in this process
which already has a negative sign, it assumes that this register has already
been reached.
After all of the accessible registers have had their signs cha ng ed, the pro-
gram goes through the area of memory reserved for the storage of list structures
and puts all the registers whose signs were not changed in the previous step
back on the free-storage list, and makes the signs of the accessible registers
positive again.
This process, because it is entirely automatic, is more convenient for the
programmer than a system in which he has to keep track of and erase un-
wanted lists. Its efficiency depends upon not coming close to exhausting the
available memory with accessible lists. This is because the reclamation process
requires several seconds to execute, and therefore must result in the addition
of at least several thousand registers to the fr ee-storag e list if the program is
not to spend most of its time in reclamation.
d. Elementary S-Functions in the Computer. We shall now describe the
computer representations of atom, = , car, cdr, and cons. An S-expression
is communicated to the program that represents a function as the location of
the word representing it, and the pro grams give S-expression answers in the
same form.
atom. As stated above, a word representing an ato mic symb ol has a special
7
We already c alled this process “garbage collection”, but I guess I chickened out of using
it in the paper—or else the Research Laboratory of Electronics grammar ladies wouldn’t let
me.
27
constant in its address part: atom is programmed as an open subroutine that
tests this part. Unless the M-expression atom[e] occurs as a condition in a
conditional expression, the symbol T or F is generated as the result of the
test. In case of a conditional expression, a conditional transfer is used and the
symbol T or F is not generated.
eq. The program for eq[e; f] involves testing for the numerical equality of
the locations of the words. This works because each atomic symbol has only
one association list. As with atom, the result is either a conditional transfer
or one of the symbols T or F .
car. Computing car[x] involves getting the contents of the address part of
register x. This is essentially accomplished by the single instruction CLA 0, i,
where the argument is in index register, and the result appears in the address
part of the accumulator. (We take the view that t he places fr om which a
function takes its arguments and into which it puts its results are prescribed
in the definition of the function, and it is the responsibility of the programmer
or the compiler to insert the required datamoving instructions to get the results
of o ne calculation in position for the next.) (“car” is a mnemonic for “content s
of the address part of register.”)
cdr. cdr is handled in the same way as car, except that the result appears
in the decrement part of the accumulator (“cdr” stands for “ contents o f the
decrement part of register.”)
cons. The value of cons[x; y] must be the location of a register that has x
and y in its address and decrement parts, respectively. There may not be such
a register in the computer a nd, even if there were, it would be time-consuming
to find it. Actually, what we do is to take the first available register from the
free-storage list, put x and y in the address a nd decrement parts, respectively,
and make the value of the function the location of the register taken. (“cons”
is an abbreviation for “construct.”)
It is the subroutine for cons that initiates the reclamation when the free-
storage list is exhausted. In the versio n of the system that is used at present
cons is represented by a closed subroutine. In t he compiled version, cons is
open.
e. Representation of S-Functions by Programs. The compilation o f func-
tions that are compositions of car, cdr, and cons, either by hand or by a
compiler program, is straightforward. Conditional expressions give no tro uble
except that they must be so compiled that only the p’s and e’s tha t are re-
28
quired are computed. However, problems arise in the compilation of recursive
functions.
In general (we shall discuss an exception), the routine for a recursive f unc-
tion uses itself as a subroutine. For example, the progra m for subst[x; y; z] uses
itself as a subroutine to eva luate the result of substituting into the subexpres-
sions car[z] and cdr[z]. While subst[x; y; cdr[z]] is being evaluated, the result
of the previous evaluation of subst[x; y; car[z]] must be saved in a temporary
storage register. However, subst may need the same register for evaluating
subst[x; y; cdr[z]]. This possible conflict is resolved by the SAVE and UN-
SAVE routines that use the p ublic push-down list
8
. The SAVE routine is
entered at the beginning of the routine for the recursive function with a re-
quest to save a given set of consecutive registers. A block of registers called
the public push-down l i st is reserved for this purpose. The SAVE ro utine has
an index that tells it how many registers in the push-down list are already
in use. It moves t he contents of the registers which are to be saved to the
first unused registers in the push-down list, advances the index of the list, and
returns to the program from which control came. This program may then
freely use these registers for temporary storage. Before the routine exits it
uses UNSAVE, which restores the contents of the temporary registers from
the push-down list and moves back the index of this list. The result of these
conventions is described, in programming terminology, by saying that the re-
cursive subroutine is transparent to the tempor ary storage registers.
f. Status of the LISP Programmi ng System (February 1960). A variant of
the function apply described in section 5f has been translated into a program
APPLY for the IBM 704. Since this routine can compute values of S-functions
given their descriptions as S-expressions and their arguments, it serves as an
interpreter for the LISP programming language which describes computation
processes in this way.
The program APPLY has been imbedded in the LISP programming system
which has the following features:
1. The progr ammer may define any number of S-functions by S-expressions.
these functions may r efer to each other or to certain S-functions represented
by machine language program.
2. The values of defined functions may be computed.
3. S-expressions may be read and printed (directly or via magnetic ta pe).
8
1995: now called a stack
29
4. Some error diagnostic and selective tracing fa cilities are included.
5. The programmer may have selected S-f unctions compiled into machine
language programs put into the core memory. Values of compiled functions
are computed about 60 times as fast as they would if interpreted. Compilation
is fast enough so that it is not necessary to punch compiled program for future
use.
6. A “program feature” allows programs containing assignment and go to
statements in the style of ALGOL.
7. Computation with floating point numbers is possible in the system, but
this is inefficient.
8. A programmer’s manual is being prepared. The LISP programming
system is appropriate for computations where the data can conveniently be
represented as symbolic expressions allowing expressions of the same kind as
sub expressions. A version of the system for the IBM 709 is being prepared.
5 Another Formalism for Functions of Sym-
bolic Expressions
There are a number of ways of defining functions of symbolic expressions which
are quite similar to the system we have adopted. Each of them involves three
basic functions, conditional expressions, and recursive function definitions, but
the class of expressions corresponding to S-expressions is different, and so are
the precise definitions of the functions. We shall describe one of these variants
called linear LISP.
The L -expressions are defined as follows:
1. A finite list of characters is a dmitted.
2. Any string of admitted characters in an L-expression. This includes the
null string denoted by Λ.
There are three functions of strings:
1. first[x] is the first character of the string x.
first[Λ] is undefined. For example: f irst[ABC] = A
2. rest[x] is the string of characters which remains when the first character
of the string is deleted.
rest[Λ] is undefined. For example: rest[ABC] = BC.
3. combine[x; y] is t he string formed by prefixing the character x to the
string y. For example: combine[A; BC] = ABC
30
There are three predicates on strings:
1. char[x], x is a single character.
2. null[x], x is the null string.
3. x = y, defined for x and y characters.
The advantage of linear LISP is that no characters are given special roles,
as are parentheses, dots, and commas in LISP. This permits computations
with all expressions that can be written linearly. The disadvantage of linear
LISP is that the extraction of subexpressions is a fairly involved, rather than
an elementary, operation. It is not hard to write, in linear LISP, functions that
correspond to the basic functions of LISP, so that, mathematically, linear LISP
includes LISP. This turns out to be the most convenient way of programming,
in linear LISP, the more complicated manipulations. However, if the functions
are to be represented by computer routines, LISP is essentially faster.
6 Flowch arts and Recursion
Since both the usual form of computer program a nd recursive function defi-
nitions are universal computationally, it is interesting to display the relation
between them. The t r anslation of recursive symbolic functions int o computer
programs was the subject of the rest of this report. In this section we show
how to go the other way, at least in principle.
The state of the machine at any time during a computation is given by the
values of a number of variables. Let these variables be combined into a vector
ξ. Consider a program block with one entrance and one exit. It defines and is
essentially defined by a certain function f that takes one machine configuration
into another, that is, f has the form ξ
′
= f (ξ). Let us call f the associated
function of the program block. Now let a number of such blocks be combined
into a program by decision elements π that decide after each block is completed
which block will be entered next. Nevertheless, let the whole prog r am still have
one entrance and one exit.
31
❄
❄
✞
✝
☎
✆
✞
✝
☎
✆
✞
✝
☎
✆
❄
❄
❄
✛
✛
❳
❳
❳③
✟
✟
✟✙
❍
❍
❍❥
✑
✑
✑✰
✲
❄
❄
f
1
f
2
f
3
f
4
π
3
π
2
π
1
S
T
Figure 5
We give as an example the flowcart of figure 5. Let us describe the function
r[ξ] that gives the transformation of the vector ξ between entrance and exit
of the whole block. We shall define it in conjunction with the functions s(ξ),
and t[ξ], which give the transformations that ξ undergoes between the points
S a nd T, respectively, and the exit. We have
r[ξ] = [π
1
1[ξ] → S[f
1
[ξ]]; T → S[f
2
[ξ]]]
S[ξ] = [π
2
1[ξ] → r[ξ]; T → t[f
3
[ξ]]]
t[ξ] = [π3I[ξ] → f
4
[ξ]; π
3
2[ξ] → r[ξ]; T → t[f
3
[ξ]]]
Given a flowchart with a single entrance and a single exit, it is easy to
write down the recursive function that gives the transformation of the state
vector from entr ance to exit in terms o f the corresponding functions for the
computation blocks and the predicates of the branch. In general, we proceed
as follows.
In figure 6, let β be an n-way branch point, and let f
1
, · · · , f
n
be the
computations leading to branch points β
1
, β
2
, · · · , β
n
. Let φ be the function
32
that transforms ξ between β and the exit of the chart, and let φ
1
, · · · , φ
n
be
the corresponding functions for β
1
, · · · , β
n
. We then write
φ[ξ] = [p
1
[ξ] → φ
1
[f
1
[ξ]]; · · · ; p
n
[ξ] → φ
n
[ξ]]]
❅
❅
❅
❅
❅
❅
☛
✡
✟
✠
☛
✡
✟
✠
☛
✡
✟
✠
✡
✡
✡
✡
✡✢
❄
❆
❆
❆
❆
❆❯
❈
❈
❈❲
❄
✂
✂
✂✌
....
.....
.....
f
1
f
2
f
n
β
β
1
β
2
β
n
φ
1
φ
2
φ
n
φ
Figure 6
7 Acknowl edgments
The inadequacy of the λ- not ation for naming recursive functions was noticed
by N. Rochester, and he discovered an alternative to the solution involving
label which has been used here. The f orm of subroutine for cons which per-
mits its composition with other functions was invented, in connection with
another pro gramming system, by C. Gerberick and H. L. Gelernter, of IBM
Corporation. The LlSP programming system was developed by a group in-
cluding R . Brayton, D. Edwards, P. Fox, L. Hodes, D. Luckham, K. Maling,
J. McCarthy, D. Park, S. Russell.
The group was supported by the M.I.T. Computation Center, and by the
M.I.T. Research Laboratory o f Electronics (which is supported in part by the
the U.S. Army (Signal Corps), the U.S. Air Force ( O ffice of Scientific Research,
Air Research a nd Development Command), and the U.S. Navy (O ffice of Naval
Research)). The author also wishes to acknowledge the p ersonal financial sup-
33
port of the Alfred P. Sloan Foundation.
REFERENCES
1. J. McCARTHY, Programs with common sense, Paper presented at the
Symposium on the Mechanization of Thought Processes, National Physical
Laboratory, Teddington, England, Nov. 2 4-27, 1958. (Published in Proceed-
ings of the Symposium by H. M. Stationery Office).
2. A. NEWELL AND J. C. SHAW, Programming the lo gic theory machine,
Proc. Western Joint Computer Conference, Feb. 1957.
3. A. CHURCH, The Calculi of Lambda-Conversi o n (Princeton University
Press, Princeton, N. J., 1941).
4. FORTRAN Progr ammer’s Reference Manual, IBM Corporation, New
York, Oct. 15, 1956.
5. A. J. PERLIS AND K. SAMELS0N, International algebraic language,
Preliminary Report, Comm. Assoc. Com p. Mach., Dec. 1958.
34
I don't understand this section about L-expressions at all, what is it trying to convey?
LISP was invented back in the 60s to make implementing symbolic AI software easier.
This conditional operator lets you have any number of clauses, like "if then else if then else if ...". It reminds me of Dijkstra's guarded command language.
This is one of the original papers that introduced LISP.
He builds upon the work of Church. So we can attribute to this paper the idea of bringing that theoretical lambda calculus stuff into practical programming.
Would 'pair' here be equivalent to what in many languages would be called zip?
Yes: "This function gives t he list of pairs of corresponding elements
of the lists x and y"
The definition seems quite unclear. I believe it is considering L-expressions to be a string. In effect, it's considering the language of L-expressions to consist of the text written down in a file.
I didn't realize the first LISP system had garbage collection built-in! That's pretty badass.