This paper defines the core of the lisp programming language. It wa...
For more background on the theory behind lisp, see "An unsolvable p...
This is written with `cond` in modern lisp.
Most lisps inherit `lambda` as an operator from here. Clojure use `...
This isn't true: you can implement recursion without labels using t...
`label` appears as `rec` in many modern Lisps.
M-expressions aren't used in lisp. These lines would be:
`(car x...
In clojure, `car` and `cdr` are `first` and `rest`. Some languages...
There is little typo on this line, it should be:
```
cons [X; A] ...
See 'An unsolvable problem of elementary number theory', by Alonzo ...
For readability:
![in clojure][1]
[1]: http://caffeinatedanal...
`fiiiirst` and `resssssst` haven't caught on.
How do you do arithmetic with pure S-expressions?
One answer is ...
`apply` and `eval` are the Maxwell's equations of lisp.
In clojure, using cadr, etc. for readability:
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 experiment s 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 t he course of its development the LISP system went through several
stages of simplification and eventually 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 a ny
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) gra nt N00014-94-1-0775
to Stanford University where John McCarthy has been since 1962. Co pied with minor nota-
tional changes from CACM, April 1960. If you want the exact typography, look there. Cur-
rent a ddress, John McCarthy, Computer Science Department, Stanford, CA 94305, (email:
jmc@cs.stanford.edu), (URL: http://www-formal.stanford.edu/jmc/ )
1
In this a r ticle, we first describe a formalism for defining functions recur-
sively. We believe this formalism has advantages both as a programming
language and as a vehicle for 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 r ole 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 the 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 are well known, but the notion of conditional
expression is believed to be new
1
, and the use of conditional expressio ns 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 elementar y functions will be defined as partial functions.
b. Propositional Expressions and Predicates. A propositional expression is
an expression 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 quant ities 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 expression has the fo r m
(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 read, “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 rules 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 encountered before any p whose value is undefined is
encountered then t he 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 for conditional expressions to a CACM forum on what should be
included in Algol 60. Because the item was s hort, 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 notatio n 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 true 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. Recursive 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 arise. 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 obtaining 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| < , may be written as
sqrt(a, x, )
= (|x
2
− a| < → x,T → sqrt ( a,
1
2
(x +
a
x
), ))
The simultaneous recursive definition of several f unctions 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 t hemselves 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. Functions and Forms. It is usual in mathematics—outside of mathe-
matical logic—to use the word “function” imprecisely and to apply it to forms
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 varia bles occurring in the form
and the ordered list of arguments of the desired function. This is accomplished
by Church’s λ-notation.
If E is a for m in variables x
1
, · · · , x
n
, then λ((x
1
, · · · , x
n
), E) will be ta ken
to be the function 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
varia bles, and λ((x, y), y
2
+ x)(3, 4) = 19.
The variables occurring in the list of varia bles 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 t he bound var ia bles 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
varia bles.
An adequate notation that distinguishes functions from forms allows an
unambiguous treatment of functions of f unctions. It wo uld invo lve too much
of a digression to give examples here, but we shall use functions with functions
as arguments later in this report.
Difficulties arise in combining functions described by λ-expressions, or by
any other notation involving variables, because 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 for com-
bining functions without the use of variables. Unfortunately, the combinatory
expressions for interesting combinations o f functions tend to be lengthy and
unreadable.
f. Expressio ns 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, ) = (|x
2
− a| < → x, T → sqrt(a,
1
2
(x +
a
x
), ))
into
sqrt = λ((a, x, ), (|x
2
− a| < → x, T → sqrt(a,
1
2
(x +
a
x
), ))),
but the right-hand side cannot serve a s an expression fo r 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 able 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 int erpreted as referring to the expression
7
as a whole. Thus we can write
label(sqrt, λ((a, x, ), (|x
2
− a| < → x, T → sqrt(a,
1
2
(x +
a
x
), ))))
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 Expressions
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 of 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 f or a given function its value
for given arguments. Finally, we shall define some functions with functions as
arguments and give some useful examples.
a. A Clas s 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 a t omic symbols a re
A
ABA
AP P LE P IE NUMBER 3
There is a twofold reason for departing from the usual mathema t ical prac-
tice of using single letters for atomic symbols. First, computer programs fre-
quently require hundreds of distinguishable symbo ls that must be fo r med from
the 47 characters that are printable by the IBM 704 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 ig nor ed. We assume only 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 are
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 within symbols, because 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 · N IL)) · (D · N IL))
((A, B), C, D · E) for ((A · (B · NIL)) · (C · (D · E)))
Since we regard the expressions with commas as abbreviations fo r those
not involving commas, we shall refer to them all as S-expressions.
b. Functions of S-expressions and the Expre s sions Th at Represent Them .
We now define a class of functions of S-expressions. The expressions represent-
ing these functions are written in a conventio nal functional notat io n. However,
in order to clearly distinguish the expressions 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, f or 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 the 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 are 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 subexpressions of a given expression in a given
position. Compositions 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 fa ct,
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 evaluatio n 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 o f the atomic symbo l y in t he 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 a r e 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 element ary 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] for car[cdr[cdr[x]]], etc.
Another useful a bbreviation 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 function of its elements.
The following functions are useful when S-expressio ns are rega rded as lists.
1. app end [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 the 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 o f 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 assumed to have the form of a list of pairs
((u
1
, v
1
), · · · , (u
n
, v
n
)), where the u’s are ato mic, and y may be any S-expression.
The value 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-Functions by S-Ex pressions. S-functions have been
described by M-expressions. We now give a rule for translating M-expressions
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 translation is determined by the following rules in rich we denote the
translation of 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 o f 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 these 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 no tation 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 the computer, it could be improved considerably.
4
f. The Universal S-Function apply. There is an S-function apply with the
property that if f is an S-expression for an S-function f
0
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
0
[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] → N IL; 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 pap e r was written in early 1959.
16
atom [e] → assoc [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 a bout 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 b e evaluated, and a list
of pairs a. The first item of each pair is an atomic symbol, and the second is
the expression fo r which the symbol stands.
3. If the expression to be evaluated is atomic, eva l evaluates whatever is
paired with it first on the list a.
4. If e is not atomic 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 (QU OT 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 (CONS, e
1
, e
2
) two
expressions have to be eva luated. In the case of (CON D, (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 front 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 comparison 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 illust ration, 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 function 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 par t of ψ beginning (COND
= eval [(COND, (π
1
,
1
), (π
2
,
2
)); ((X, (QUOTE, ( A·B) ) ), (FF, φ) )]
Denoting ((X, (QUOTE, (A·B))), (FF, φ)) by a, we obtain
= evcon [((π
1
,
1
), (π
2
,
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
,
2
, )); a],
which involves eval [π
2
; a] = eval [(QUOTE, T); a] = T
Our main calculation again continues with
apply [φ; ((A·B))]
= eval [
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 fro m the earlier computation of atom [eval [X; a]] = A,
and so evlis [((CAR, X)); a] then becomes
list [list [QUOTE; A]] = ((QUOTE, A)),
and our main quantity becomes
= eval [(φ, (QUOTE, A)); a]
The subsequent steps are made as in the beginning of the calculation. The
LABEL and LAMBDA cause new pairs to be added to a, which gives a new
list of pairs a
1
. The π
1
term of the conditional eval [(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
] from the evcon, and this is just A.
g. Func tion s with Functions as Arguments. There are a numb er of useful
functions some of whose argument s ar e functions. They are especially useful
in defining other f unctions. 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 for the partial derivative
with r espect to x of expressions involving sums and products of x and other
varia bles. The S-expressions t hat 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] → O NE; 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 formula, 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 argument s 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 t o
compute with symbolic information in the form of S- expressions. It has been
or will be used for the f ollowing purposes:
l. Writing a compiler to compile LISP programs into machine language.
2. Writing a program to check proofs in a class of f ormal logical systems.
3. Writing programs for formal differentiation and integration.
4. Writing progra ms to realize various algorithms for generating proofs 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 progra ms written as sequences of statements along the
lines of FORTRAN (4) or ALGOL (5). These features will not be described
in this article.
a. Representation of S-Expressions by List Structure. A list structure is a
collection of computer wo r ds arranged a s in figure 1a or 1b. Each word of the
list structure is represented by one of the subdivided rectangles in the figure.
The lef t box of a rectangle represents the address field of the word and the
right box represents the decrement field. An arrow from a box to another
rectangle means that the field corresp onding 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 pro gram to
tell that this word 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 the 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 form of S-expressions, we see that the S-expressio n (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 t erm
of the list occupies the address part of a word, the decrement part of which
points to the wo r d 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 repeated depends upon the history of the pro-
gram. Whether or not a subexpression is repeated will make no difference
in the results of a program as they appear outside the machine, although it
will a ff ect the time and storage requirements. For 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 a gainst circular list structures is essentially a prohibition
24
against an expression being a subexpression o f itself. Such an expression could
not exist on paper in a world with our topology. 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 t he program
will have to deal cannot be predicted in advance. Therefore, it is difficult to
arrange blocks of storage of fixed length to contain 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 blo cks of regist ers of odd sizes
that may become available.
3. An expression that occurs as a subexpression of several expressions need
be represented in storage o nly once.
b. Association Lists
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 b e put on the association list. This information
may include: the print name, that 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-expression if the symbol, in some way, serves
as a name for it; or the locatio n of a routine if the symbol 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 b een described in
the sections on the mathematical system.
For the present, we shall only describe how print name s a re 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 a nd 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 tha t the structure f or the print name
of the symbol whose association list this is hanging fro m 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 pro perty lists.
25
six 6-bit characters. The last word is filled out with a 6-bit combination that
does no t represent a character printable by the computer. (Recall that the
IBM 7O4 has a 36-bit word and that printable cha racters are each represented
by 6 bits.) The presence of the wor ds with cha racter 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 ,0 00) 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 changed to
become the location of the second word on the free-storage list. No provision
need be made for the user to prog r am the return of registers to the free-storage
list.
This r eturn 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 pro gram. Of course, because list struc-
tures branch, an arbitrary numb er of registers 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 prog ram 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 there is none left on the free-storage list, a reclamation
7
cycle starts.
First, the progr am 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 t hat can be reached fro m
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 ha s already
been reached.
After all of the accessible registers have had their signs changed, the pro-
gram goes through the area of memory r eserved fo r 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, a nd 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 free-storage list if the program is
not to spend most of its time in reclamation.
d. Elementary S-Functions in the Com puter. We shall now describe the
computer representations of atom, = , car, cdr, and cons. An S-expression
is communicated to the pro gram that represents a function as the location of
the word representing it, and the programs give S-expression a nswers in the
same form.
atom. As stated above, a word representing an ato mic symbol has a special
7
We already called this process “garbage collection”, but I guess I chickened o ut 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, t he 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 arg ument is in index register, and the result appears in the address
part of the accumulator. (We take the view that the places from 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 “contents
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 of 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 and, 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 l i st, put x and y in the address and 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 version of the system that is used at present
cons is represented by a closed subroutine. In the compiled version, cons is
open.
e. Representation of S-Functions by Programs. The compilation of func-
tions that are compositions of car, cdr, and cons, either by hand or by a
compiler program, is straightforward. Conditional expressions g ive no trouble
except that they must be so compiled that only the p’s and e’s that 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 func-
tion uses itself as a subroutine. For example, the program for subst[x; y; z] uses
itself as a subroutine to evaluate 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 o f 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 r outines that use the public 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 lis t is reserved for this purpose. The SAVE r outine has
an index that tells it how many registers in the push-down list are already
in use. It moves the 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 r egisters 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 tra nsparent to the temporary storage r egisters.
f. Status of the LISP Progra mming 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 prog r amming 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 refer to each other or t o 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 tape).
8
1995: now called a stack
29
4. Some error diagnostic and selective tracing facilities are included.
5. The programmer may have selected S-functions compiled into machine
language programs put into t he core memory. Values of compiled functions
are computed about 60 times as fa st 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 b eing prepared. The LISP programming
system is appropriate for computations where the data can conveniently be
represented as symbo lic 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 Fu nctions of Sym-
boli c 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 fo llows:
1. A finite list of characters is admitted.
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: first[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 the 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 chara cter.
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 Recurs i on
Since both the usual form of computer program and recursive function defi-
nitions a r e universal computationally, it is interesting to display the relation
between them. The translation of recursive symbolic functions into 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
0
= 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 program still have
one entra nce and one exit.
31
?
?
?
?
?
X
X
Xz
H
H
Hj
+
-
?
?
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 o f figure 5. Let us describe the function
r[ξ] that gives the tra nsformation of the vector ξ between entrance a nd 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 and 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 entra nce and a single exit, it is easy to
write down the recursive function that gives the transformation of the state
vector from ent rance to exit in terms of 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]]]
@
@
@
@
@
@
?
A
A
A
A
AU
C
C
CW
?
....
.....
.....
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-notation for naming recursive f unctions was noticed
by N. Rochester, a nd he discovered an alternative to the solution involving
label which has been used here. The form of subroutine for cons which per-
mits its composition with other functions was invented, in connection with
another programming system, by C. Gerberick and H. L. Gelernter, of IBM
Corporation. The LlSP prog r amming 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 Cent er, and by the
M.I.T. Research Labora tory of 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 personal 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. 24-27 , 1958. (Published in Proceed-
ings of the Symposium by H. M. Stationery Office).
2. A. NEWELL AND J. C. SHAW, Programming the log ic theory machine,
Proc. Western Joint Computer Conference, Feb. 19 57.
3. A. CHURCH, The Calculi of Lambda-Convers i on (Princeton University
Press, Princeton, N. J., 1941).
4. FORTRAN Programmer’s Reference Manual, IBM Corporation, New
York, Oct. 15, 1956.
5. A. J. PERLIS AND K. SAMELS0N, International algebraic language,
Preliminary Report, Comm. Assoc. Comp. Mach., Dec. 1958.
34
Just `map`, usually
Both circular sequences and infinite sequences (e.g. the sequence of all integers) are useful. It's not a good idea to print them, though.
Correction:
...whose association list this is, hangs from the next word...
`label` appears as `rec` in many modern Lisps.
`apply` and `eval` are the Maxwell's equations of lisp.
This is true only if your structures are immutable. Everything is immutable in clojure, but not in most lisps.
Subtle issues like this are why mutability should be used sparingly in any language: to avoid unpleasant surprises.
No I was confused, it is the familiar `let` not `rec`.
Lot of typographical issues here, see original paper for the correct version.
`let rec` and `rec` are equivalent and just `let` won't do for defining recursive functions. Looking back at this after couple weeks. :)
In modern terms, a 'closed' subroutine is a regular function, and an 'open' subroutine is an inlined function.
Implementing `eval` in terms of itself might feel like cheating. More directly, eval uses `car` to implement `car`, which doesn't seem like it would work.
The trick is something called a bootstrapped compiler: once you have a lisp, or any other language, you can write a compiler for the language in itself. The very first compiler has to be written in some other language, but it can be very basic, and you can throw out the source for that as soon as it runs.
(This works for interpreters too.)
The first lisp interpreter was implemented in machine code for the IBM 704.
`fiiiirst` and `resssssst` haven't caught on.
For readability:
![in clojure][1]
[1]: http://caffeinatedanalytics.com/images/mccarthy_code/1.png
(Expanded with clojure syntax. This uses the rainbow-delimiters emacs plugin, which colors parentheses by nesting depth.)
There is little typo on this line, it should be:
```
cons [X; A] = (X . A)
```
(missing dot)
(car (cons '(A.B) x), right?
M-expressions aren't used in lisp. These lines would be:
`(car x)`
`(car (cons '(A B) x))`
See 'An unsolvable problem of elementary number theory', by Alonzo Church for proof that this covers all computable functions, and 'Computability and $\lambda$-definability ' by Turing for proof of equivalence to a Turing machine specifically.
How do you do arithmetic with pure S-expressions?
One answer is treating the length of a list as an integer. Specifically, we can define the empty list as 0, take some variable, say `S`, and define 1 to be `'(S)`, two to be `'(S S)`, etc.
You can start defining arithmetic on these numbers:
![arithmetic on s-expressions][2]
[2]: http://caffeinatedanalytics.com/images/mccarthy_code/2.png
The runtime, of course, is atrocious.
i.e. the heap
Most lisps inherit `lambda` as an operator from here. Clojure use `fn` instead.
This isn't true: you can implement recursion without labels using the y combinator. For a great explanation, see http://mvanier.livejournal.com/2897.html
If you've made it this and you don't know lisp yet, try [Clojure for the Brave and True](http://www.braveclojure.com/) (if you have a little time) or [Structure and Interpretation of Computer Programming](https://mitpress.mit.edu/sicp/) (if you have a lot of time).
I think McCarthy here was just referring to inadequacy of "naming" recursive functions with \lambda. See comment about combinators in the previous paragraph.
This is written with `cond` in modern lisp.
For more background on the theory behind lisp, see "An unsolvable problem of elementary number theory" by Alonzo Church.
This paper defines the core of the lisp programming language. It was intended as a theoretical exercise until Steve Russell, one of McCarthy's grad students, wrote a working lisp interpreter in machine code - much to McCarthy's surprise.
In clojure, `car` and `cdr` are `first` and `rest`. Some languages use `head` and `tail`.
In clojure, using cadr, etc. for readability:
![apply + eval in clojure][3]
[3]: http://caffeinatedanalytics.com/images/mccarthy_code/3.png