INTRODUCTION TO LISP

An Introduction to LISP
• Introduction
• LISP: A Brief Overview
– Expressions, the Syntactic Basis of LISP
– Control of LISP Evaluation: quote and eval
– Programming in LISP: Creating New Functions
– Program Control in LISP: Conditionals and Predicates
– Functions, Lists, and Symbolic Computing
– Lists as Recursive Structures
– Nested Lists, Structure, and car/cdr Recursion
– Binding Variables Using set
– Defining Local Variables Using let
– Data Types in Common LISP

Symbolic
• Why do we care about symbols?
– Understand human cognition with a language like
symbolic processing

Physical Symbol System Hypothesis
"A physical symbol system has the necessary
and sufficient means for general intelligent
action." (Newell & Simon 1976)
• Physical symbol system
– Set of entities called symbols - physical patterns
that can occur as components
– Expressions (or symbol structures) built of
symbols

Characteristics of LISP
1. Language for artificial intelligence programming
a. Originally designed for symbolic computing
2. Imperative language
a. Describe how to perform an algorithm
b. Contrasts with declarative languages such as PROLOG
3. Functional programming
a. Syntax and semantics are derived from the mathematical theory of recursive
functions.
b. Combined with a rich set of high-level tools for building symbolic data
structures such as predicates, frames, networks, and objects
4. Popularity in the AI community
a. Widely used as a language for implementing AI tools and models
b. High-level functionality and rich development environment make it an ideal
language for building and testing prototype systems.

LISP: A Brief Overview
• Syntactic elements of LISP
– Symbolic expressions : S-expressions
• Atom : basic syntactic units
• List
– Both programs and data are represented as s-expressions
• Atom
– Letters (upper, lower cases)
– Numbers
– Characters : * + - / @ $ % ^ & _ < > ~ .
– Example
• 3.1416
• Hyphenated-name
• *some-global*
• nil
• x

SPECIAL FORMS:
1. Forms not evaluated according to the
evaluation rule.
2. Special forms:
defun, defparameter, setf, let, case, if,
function, quote.

OTHER DATA TYPES
1. Strings: (length "abc") --> 3
2. Many number types.

LISP EVALUATION RULE
1. Every expression is either a list or an atom.
2. Every list to be evaluated is either a special form or a function
application.
3. A special form expression is a list whose first element is a special form
operator and is evaluated according to the special rule of that operator.
4. A function application is evaluated by first evaluating the arguments (the
rest of the list) and then finding the function named by the first
element of the list and applying it to the list of evaluated arguments.
5. Every atom is either a symbol or a non-symbol.
6. A symbol evaluates to the most recent value assigned to the variable.
7. A non-symbol atom evaluates to itself.

Read-Eval-Print
• Interactive Environment
– User enters s-expressions
– LISP interpreter prints a prompt
• If you enter
– Atom: LISP evaluates itself (error if nothing is bound
to the atom)
– List: LISP evaluates as an evaluation of function, i.e.
that the first item in the list needs to be a function
definition (error if no function definition is bound for
the first atom), and remaining elements to be its
arguments.
• > (* 7 9)
63

Control of LISP Evaluation:
quote & eval
• quote : ‘
– prevent the evaluation of arguments
• (quote a) ==> a
• (quote (+ 1 3)) ==> (+ 1 3)
• ‘a ==> a
• ‘(+ 4 6) ==> (+ 4 6)
• eval
– allows programmers to evaluate s-expressions at will
• (eval (quote (+ 2 3))) ==> 5
• (list ‘* 2 5) ==> (* 2 5)
• (eval (list ‘* 2 5)) ==> 10

WHAT MAKES LISP DIFFERENT
1. Built-in support for Lists.
2. Automatic memory management.
3. Dynamic typing.
4. First-class functions.
5. Uniform syntax.
6. Extensibility

Why LISP?
– Especially designed for symbol manipulation.
– Provides built-in support for lists (“everything is a
list..”)
– Automatic storage management (no need to keep
track of memory allocation).
– Interactive environment, which allows programs
to be developed step by step. That is, if a change
is to be introduced, only changed functions need
to be recompiled.

Interpreted & interactive
• Interpreted
• Interactive USER(1): 12
12
USER(2): (+ 12 3)
15
USER(3): (setf Almost-age 31)
31
USER(4): Almost-age
31
USER(5): 'Almost-age
ALMOST-AGE
USER(6):

LISTS
1. Primitive aggregating type.
2. Primitive functions: first, second, ..., length,
last. append, cons, list.

List and S-expression
• List
– A sequence of either atoms or other lists separated by
blanks and enclosed in parentheses.
• Example
– (1 2 3 4)
– (a (b c) (d (e f)))
– Empty list “( )” : nil
• nil is the only s-expression that is considered to be both an
atom and a list.
• S-expression
– An atom is an s-expression.
– If s1, s2,…, sn are s-expressions,
then so is the list (s1 s2 … sn).

Everything's a List!
• Data
• Functions
• Simple syntax:
(function-name arg1 arg2 …)
(a b c)
(defun plus (x y)
(+ x y))

List as recursive structures
• Cons cell: data structure to hold a list in LISP
– car - holds the first element.
– cdr - holds the rest in the list.
• Accessing a list
– (car ‘(a b c)) ==> a
– (cdr ‘(a b c)) ==> (b c)
– (first ‘(a b c)) ==> a
– (second ‘(a b c)) ==> b
– (nth 1 ‘(a b c)) ==> b
• Constructing a list
– (cons ‘a ‘(b c)) ==> (a b c)
– (cons ‘a nil) ==> (a)
– (cons ‘(a b) ‘(c d)) ==> ((a b) c d)
Artificial Intelligence 17

Nested lists, structure,
car/cdr recursion
• More list construction
– (append ‘(a b) ‘(c d)) ==> (a b c d)
– (cons ‘(a b) ‘(c d)) ==> ((a b) c d)
• Counting the number of elements in the list
– (length ‘((1 2) 3 (1 (4 (5))))) ==> 3
Artificial Intelligence 18

Dynamic
• Functions are first-class objects
– Pass functions as arguments to other functions
USER(2): (apply #'+ '(1 2 3))
6
USER(1): (+ 1 2 3)
6

One Function, and a Side of Fries, To Go
• Create functions on the fly
• Lisp contains itself: eval
USER(1): (funcall #'(lambda (x y) (+ x y))
17 14)
31

Name Calling
• Lisp remembers function names separately from
variable names
USER(22): (defun add (x y) (+ x y))
ADD
USER(23): (setf add 9)
9
USER(24): add
9
USER(25): #'add
#<Interpreted Function ADD>

Basic terminology
• Atoms: word-like indivisible objects which can be numbers or symbols.
• Lists: sentence-like objects formed from atoms or other lists, and
enclosed in parentheses.
• S-expressions: compositions of atoms and lists.
• Procedures: step by step specifications how to do something.
– Primitives: procedures supplied by the LISP itself
Example: (+ 5 6)
– User-defined procedures: procedures introduced by the programmer.
Example: (students 'anna)
• Program: a collection of procedures working together.

S-expressions
• An s-expression can have other s-expressions nested in it. Examples:
(+ (* 5 7) ( / 2 4))
(This (is a dog) (or a cat))
• Expressions can be interpreted both, procedurally and declaratively.
– If interpreted procedurally, an expression provides a direction for
doing something. Such an expression is called a form, and its first
element is the name of a procedure to be used to produce the
value.
– The process of computing the value of an expression is called
evaluation.
– If interpreted declaratively, expressions represent data.
Data and procedures have the same syntax.

Evaluation of atoms
• The value of a number is the number itself.
Example: 5 ==> 5
• The value of a string is the string itself.
Example: “Nice day” ==> “Nice day”
• The value of the symbol T is T (true).
• The value of the symbol NIL is NIL (false).
• The symbol NIL and the empty list ( ) are the same thing.
• Variables are names of memory locations. The contents stored in a given
memory cell is the value of the variable serving as a name of this location.
Example: Let x be a variable, and 5 be the contents of the memory cell called x.
Then, the value of x is 5.

Numbers
• Integers: 179, 45
• Ratio: 5/7, 7/9
• Floating point: 5.2, 7.9
• Examples:
* (/ 25 5)
5
* (/ 46 9)
46/9 ; do not divide evenly
* (float (/ 46 9))
5.111111
* (round (/ 46 9))
5 ; the nearest integer
1/9 ; the remainder

More numeric primitives
* (- 6)
-6
* (- -6)
6
* (max 5 7 2)
7
* (min 5 7 2)
2
* (sqrt (* (+ 1 3) (* 2 2)))
4.0
* (+ (round (/ 22 7)) (round (/ 7 3)))
5
* (+ 2 2.5)
4.5
* (expt 3 6)
729
* (sqrt 81)
9.0
* (sqrt 82)
9.055386
* (abs 6)
6
* (abs -6)
6

Conditionals & Predicates(1/2)
• Conditions
– (cond (<condition 1> <action1>)
(<condition 2> <action 2>)
...
(<condition n> <action n))
– Evaluate the conditions in order until one of the condition
returns a non-nil value
– Evaluate the associated action and returns the result of the
action as the value of the cond expression
• Predicates
– Example
• (oddp 3) ; whether its argument is odd or
not
• (minusp 6)
• (numberp 17)

Conditionals & Predicates(2/2)
• Alternative Conditions
– (if test action-then action-else)
• (defun absolute-value (x)
(if (< x 0) (- x) x))
• it returns the result of action-then if test return a non-nil value
• it return the result of action-else if test returns nil
– (and action1 ... action-n) ;
conjunction
• Evaluate arguments, stopping when any one of arguments evaluates to nil
– (or action1 ... action-n) ;
disjunction
• Evaluate its arguments only until a non-nil value is encountered

DEFINING NEW FUNCTIONS:
1. (defun <name> (<parameters>)
"doc"
<body>)
(defun last-name (name)
"Select last name from a name represented
as a list."
(first (last name)))
(last-name '(john r vandamme)) --> vandamme
(last-name '(john quirk MD)) --> MD
2. Importance of abstraction.
(defun first-name (name) (first name))

Creating New
Functions
• Syntax of Function Definition
– (defun <function-name> (<formal parameters>)
<function body>)
• defun : define function
• Example
– (defun square (x)
(* x x))
– (defun hypotenuse (x y) ;the length of the hypotenuse is
(sqrt (+ (square x) ;the square root of the sum of
(square y)))) ;the square of the other sides.

USING FUNCTIONS:
1. (setf names '((john x ford) (peter p rabbit) (fabianna f
wayne)))
(mapcar #'last-name names) --> (ford rabbit wayne)
#' from name of function to function object.
mapcar primitive.
2. (defparameter *titles*
'(Mr Mrs Miss Madam Ms Sir Dr Admiral Major General))
(defun first-name (name)
"Select first name from a list representing a name."
(if (member (first name) *titles*)
(first-name (rest name))
(first name)))
(if <test> <then-part> <else-part>)
(first-name '(Madam Major General Paula Jones)) --> Paula
3. Trace functions

HIGHER ORDER FUNCTIONS
1. Functions as first-class objects: can be manipulated,
created, modified by running code.
2. Apply: (apply #'+ '(1 2 3 4)) --> 10
3. Funcall: (funcall #'+ 1 2) --> 3
(funcall #'+ '(1 2)) -->
error (1 2) is not a number.
4. Function constructor: lambda.
(lambda (parameter ...) body...):
non atomic name of a function.
((lambda (x) (+ x 2)) 4) --> 6
(funcall #'(lambda (x) (* x 2)) 4) --> 8
***Can create functions at run time.***

Binding variables: set(1/2)
• (setq <symbol> <form>)
– bind <form> to <symbol>
– <symbol> is NOT evaluated.
• (set <place> <form>)
– replace s-expression at <place> with <form>
– <place> is evaluated. (it must exists.)
• (setf <place> <form>)
– generalized form of set: when <place> is a symbol, it behaves like setq; otherwise,
it behaves like set.

 Examples of set / setq
 (setq x 1) ==> 1 ;;; assigns 1 to x
 (set a 2) ==> ERROR!! ;;; a is NOT defined
 (set ‘a 2) ==> 2 ;;; assigns 2 to a
 (+ a x) ==> 3
 (setq l ‘(x y z)) ==> (x y z)
 (set (car l) g) ==> g
 l ==> (g y z)
 Examples of setf
 (setf x 1) ==> 1
 (setf a 2) ==> 2
 (+ a x) ==> 3
 (setf l ‘(x y z)) ==> (x y z)
 (setf (car l) g) ==> g
 l ==> (g y z)
Binding variables: set(2/2)

Local variables: let(1/2)
• Consider a function to compute roots of a quadratic equation:
ax2+bx+c=0
– (defun quad-roots1 (a b c)
(setq temp (sqrt (- (* b b) (* 4 a c))))
(list (/ (+ (- b) temp) (* 2 a))
(/ (- (- b) temp) (* 2 a))))
– (quad-roots1 1 2 1) ==> (-1.0 -1.0)
– temp ==> 0.0
• Local variable declaration using let
– (defun quad-roots2 (a b c)
(let (temp)
(setq temp (sqrt (- (* b b) (* 4 a c))))
(list (/ (+ (- b) temp) (* 2 a))
(/ (- (- b) temp) (* 2 a)))))

 Any variables within let closure are NOT bound at top level.
 More improvement (binding values in local variables)
 (defun quad-roots3 (a b c)
(let ((temp (sqrt (- (* b b) (* 4 a c))))
((denom (*2 a)))
(list (/ (+ (- b) temp) denom)
(/ (- (- b) temp) denom))))
Local variables: let(2/2)

Looping Constructs -- Recursion
Are there enemies in the list of attendees
of King Arthur’s party tonight?
(setf guest-list ‘(micky snowwhite bishop
robocop sirlancelot ))
(setf enemies ‘(robocop modred)
Is there an ‘enemies’ in ‘guest-list’?
How do we find out?

What the function should do
Given a the function and a list of guests,
return ‘t’ if there is an enemy in the list.
(find-enemy guest-list enemies)
T
It should return t if the program finds an
enemy in the list.

Recursive Functions
• Functions that call themselves
• A partial LISP version of Martin’s solution
(defun find-enemy (guest-list enemies)
(cond ( (member (first guest-list) enemies) T ) ; Yes, there is an enemy
( T (find-enemy (rest guest-list enemies) ) ) ; this is the recursion
) ; end of cond
) ; end of defun
In English: If the first person on the guest-list is a member of
the enemies list, then return true, otherwise call the
function again with the rest of the list. But, this isn’t very
good. What if there are no guests who are enemies – you’ll
try to keep going until you get an error…..

Fixing the infinite recursion
Need to stop the recursion . In this case
we use a null! But there are other ways
(test for an atom? Test for something
else).
(defun find-enemy ( guest-list enemies)
(cond ( (null guest-list) NIL ) ; end of the list
( (member (first guest-list) enemies) T ) ; Yes, there is
( T (find-enemy (rest guest-list ) enemies) )
) ; end of cond
) ; end of defun

What does this look like?
• (find-enemy guest-list enemies)
– guest-list = ‘(micky snowwhite bishop robocop sirlancelot ))
– enemies = (robocop modred) (this is always the same so I won’t
repeat it)
– guest-list = ‘(snowwhite bishop robocop sirlancelot)
– guest-list = (bishop robocop sirlancelot)
– guest-list = (robocop sirlancelot) -> this is where it finds robocop
and stops

Next version of program
What are the names of those enemies that are
in the attendee list? Right now, it only tells
me if there is a single enemy – and then it
stops. But there could be more than one, and
we would want to know who they are.
So, now I can’t just return true – I need to make
a separate list of the enemies as I go down
the list….

Building lists using recursion
This is the same program, but now we need to do something instead of return
‘t’…..This code will go where the ? Is
(defun identify-enemy ( guest-list enemies)
(cond ( (null guest-list) NIL ) ; the end of the guest list.
( (member (first guest-list) enemies) ?what to do? )
(t (identify-enemy (rest guest-list ) enemies) )
) ; end of cond
) ; end of defun
We need to add something that will build a list of the enemies…..
How do we build lists?????-> we use cons

The answer ---
(defun identify-enemy ( guest-list enemies)
(cond ( (null guest-list) NIL ) ; Reached the end of the guest list.
( (member (first guest-list) enemies)
(cons (first guest-list)
(identify-enemy (rest guest-list) enemies))) ;
( T (identify-enemy (rest guest-list ) enemies ) )
) ; end of cond
) ; end of defun

What does this look like?
– guest-list = ‘(micky snowwhite bishop robocop sirlancelot ))
– enemies = (robocop modred) (this is always the same so I won’t
repeat it)
– guest-list = ‘(snowwhite bishop robocop sirlancelot)
– guest-list = (bishop robocop sirlancelot)
– guest-list = (robocop sirlancelot) -> this is where it finds robocop
and calls ‘cons’ --- BUT BEFORE actually do the cons, I make the
recursive call with….

– guest-list = (sirlancelot)
• (find-enemey guest-list enemies)
– guest-list = () -> The (null guest-list ) is executed and nil is
returned….but wait, I’ve got to finish my cons….
– So the () is returned to the (cons (first guest-list) to …..and the
(robocop) gets returned from the function….

Other uses of recursion
It can be used for filtering – example, removing
negative numbers from a list
( defun filter-negatives (ls)
(cond ((null ls) '())
((< (first ls) 0) (filter-negatives (rest ls)))
(t (cons (first ls) (filter-negatives (rest ls))))))
(filter-negatives ‘(-50 0 50 100 150)) ===> (0 50 100 150)
In English: If the first of the list is less than 0, call the function with the rest of
the list (eliminating the negative number), else cons that first of the list to
the recursive call…

Recursion can be used to count
(defun count-atoms (ls)
(cond ((null ls) 0)
(t (+ 1 (count-atoms (rest ls))))))
This is the same as length.
(count-atoms ‘(one two three four))
4
English: When the list is empty return 0, else add 1 every time you call the
recursive function (it will add as it returns to the calling function.

Recursion Problem
Write a function called stop that takes a list and
a number and returns a list that does not
exceed the number.
For example:
(stop ‘(this is a list for recursion) 4) -> (this is a
list)

(stop ‘(this is a list for recursion) 4) -> (this is a
list)
• We need to define a function that will take
two arguments
• How are we going to stop it?
• We want to return a list….so we will need to
create a new list (which means a cons)….

Now, back to our guests…..
Let’s go back to the guests…..What if the
guests come in subgroups such as…..
((Micky SnowWhite) Bishop (BlackKnight
WhiteKnight) RoboCop ( (SirLancelot
Modred) Magician) )
How do we write a function to find the
enemies in that type of list?

Recursion on both head and tail
(defun identify-enemy ( guest-list )
(cond ( (null guest-list) NIL ) ; Reached the end
( (listp (car guest-list)) ; If the first element is a list
(append (identify-enemy (first guest-list))
(identify-enemy (rest guest-list))) )
( (member (first guest-list) enemies)
; add to the list of enemies identified from the rest
; of guests
(cons (first guest-list)
(identify-enemy (rest guest-list) ) ))
( T (identify-enemy (rest guest-list) ) )
) ) ; end of defun

Keys to programming recursive
functions
• Find out how to solve simple special cases
– How to handle empty list?
– Are all possible ways to terminate the recursion considered?
• Break the task down into smaller subtasks (e.g., using recursion with a
combination of first/rest)
• Know how to synthesize partial solutions generated by subtasks into an
overall solution. (e.g., using CONS, APPEND, LIST, +, etc.)
• Look at the arguments separately, and determine what needs to be passed
into the function – which arguments need to change and how should they
be changed (e.g., decremented, added, rest, etc.
• If all else fails, make ‘two’ functions --- one calls the other to do the work.

How to declare local variables (we are going to need this for
iterative functions)?
(LET ( ( <var1> <val1> )
...
( <vark> <valk> ) )
<exp1>
...
<expN> )
• LET is your way to set up temporary variables. You
can initialize each local variable to its value
concurrently, then evaluates expressions sequentially.
It returns the result of evaluating the last expression.
• The default value of local variables declared by LET is
NIL.

Recursion Templates
• Recursion template
– A few standard forms of recursive Lisp functions.
– You can create new functions by choosing a template and filling in the blanks.
• Recursion templates
① Double-Test Tail Recursion
② Single-Test Tail Recursion
③ Single-Test Augmenting Recursion
④ List-Consing Recursion
⑤ Simultaneous Recursion on Several Variables
⑥ Conditional Augmentation
⑦ Multiple Recursion
⑧ CAR/CDR Recursion

Double-Test Tail Recursion
• Template :
(DEFUN func (X)
(COND (end-test-1 end-value-1)
(end-test-2 end-value-2)
(T (func reduced-x))))
• Example :
Func : ANYODDP
End-test-1 : (NULL X)
End-value-1 : NIL
End-test-2 : (ODDP(FIRST X))
E nd-value-2 : T
Reduced-x : (REST X)
(defun anyoddp (x)
(cond ((null x) nil)
((oddp (first x)) t)
((t (anyoddp (rest x))))

Single-Test Tail Recursion
• Template :
(DEFUN func (X)
(COND (end-test end-value)
• Example :
Func : FIND-FIRST-ATOM
End-test : (ATOM X)
End-value : X
Reduced-x : (FIRST X)
(defun find-first-atom (x)
(cond ((atom x) x)
((t (find-first-atom (first x))))

Single-Test Augmenting Recursion(1/2)
 Template :
(DEFUN func (X)
(T (aug-fun aug-val)
(func reduced-x))))
 Example1 :
Func : COUNT-SLICES
End-test : (NULL X)
End-value : 0
Aug-fun : +
Aug-val : 1
(defun count-slices (x)
(cond ((null x) 0)
(t (+ 1 (count-slices (rest x))))))

Single-Test Augmenting Recursion(2/2)
 Example2: The Factorial Function
(defun fact (n)
(cond ((zerop n) 1)
(t (* n (fact (- n 1))))))

List-Consing Recursion
(A Special Case of Augmenting Recursion)
• Template :
(DEFUN func (N)
(COND (end-test NIL)
(T (CONS new-element
(func reduced-n)))))
• Example :
Func : LAUGH
End-test : (ZEROP N)
New-element : ‘HA
Reduced-n : (- N 1)
(defun laugh (n)
(cond ((zerop n) nil)
(t (cons ‘ha (laugh (- n 1))))))

Simultaneous Recursion on Several Variables
(Using the Single-Test Recursion Template)
• Template :
(DEFUN func (N X)
(T (func reduced-n reduced-x))))
• Example :
Func : MY-NTH
End-test : (ZEROP N)
End-value : (FIRST X)
Reduced-n : (- N 1)
(defun my-nth (n x)
(cond ((zerop n) (first X)
(t (my-nth (- n 1) (rest x)))))

Conditional Augmentation (1/2)
• Template
(DEFUN func (X)
(aug-test (aug-fun aug-val
(func reduced-x)))
• Example
Func : EXTRACT-SYMBOLS
End-test : (NULL X)
End-value : NIL
Aug-test : (SYMBOLP (FIRST X))
Aug-fun : CONS
Aug-val : (FIRST X)

Conditional Augmentation (2/2)
(defun extract-symbols (X)
(cond ((null x) nil)
((symbolp (first x))
(cons (first x)
(extract-symbols (rest x))))
(t (extract-symbols (rest x)))))

Multiple Recursion(1/2)
• Template :
(DEFUN func (X)
(COND (end-test1 end-value-1)
(end-test2 end-value-2)
(T (combiner (func first-reduced-n)
(func second-reduced-n)))))
• Example :
Func : FIB
End-test-1 : (EQUAL N 0)
End-value-1 : 1
End-test-2 : (EQUAL N 1)
End-value-2 : 1
Combiner : +
First-reduced-n : (- N 1)
Second-reduced-n : (- N 2)

Multiple Recursion(2/2)
(defun fib (n)
(cond (equal n 0) 1)
(equal n 1) 1)
(t (+ (fib (- n 1))
(fib (- n 2))))))

CAR/CDR Recursion
(A Special Case of Multiple Recursion)(1/2)
• Template :
(DEFUN func (X)
(COND (end-test-1 end-value-1)
(end-test-2 end-value-2)
(T (combiner (func CAR X))
(func (CDR X))))))
• Example :
Func : FIND-NUMBER
End-test-1 : (NUMBERP X)
End-value-1 : X
End-test-2 : (ATOM X)
End-value-2 : NIL
Combiner : OR

CAR/CDR Recursion
(A Special Case of Multiple Recursion)(2/2)
(defun find-number (x)
(cond ((numberp x) x)
((atom x) nil)
(t (or (find-number (car x))
(find-number (cdr x))))))

An Example of LET
Is the situation of the party likely to be safe for you ? That is, are
there more ‘friends’ then ‘enemies’ at the party!
(defun is-safe-p ( guests )
(let ( (friendly-guests nil)
(hostile-guests nil) )
(setq friendly-guests (identify-friends guests))
(setq hostile-guests (identify-enemy guests))
(> (length friendly-guests)
(length hostile-guests) )
) )

Let*
Let* evaluates ALL of the expressions before ANY of the
variables are bound. Let* allows you to evaluate
things in the order that they are listed (this is not
necessarily true of LET)
Example:
(let* ((sum (+ 8 3 4 2 7)) ; sum needs value
(mean (/ sum 5)))
(* mean mean))
***Most of the time it doesn’t matter, but check this if
you keep getting errors.

Iteration
• The simplest form of iteration is the – LOOP
(loop
(<test condition> ) (return <optional var/val)
[body]
) ;end loop

Example Loop
(let ((counter 1)) ; initializing my variable
(loop
(If (= counter 2) (return (+ counter 5))
(+ counter 1))))
6 is returned.

Another Example
(defun test ()
(let ((n 0) (lis nil)) ; initializing
(loop
(if (> n 10) (return lis)
(setq lis (cons n lis))) ; this is end
of if
(setq n (+ n 1)))))
=> (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

dotimes
Use dotimes for a counted loop
(dotimes (<var--count> <intobj- upper limit> [retobj>])
<body>)
(defun test ()
(let ((sum 0))
(dotimes (x 5 sum) ; x var initialized to 0; 5 is limit; sum is return
(print x) (print sum)
(setq sum (+ sum x)))))

Dolist
Use DOLIST to process elements of a list.
This is like cdring down the list –
recursion!
(dolist (<var><list> [<retobj>]
<body>)
Evaluates <list> ;which must be a list
The var is then bound to each item in list

Example dolist
(defun sq-list (lst1)
(let ((lst2 nil))
(dolist (ele lst1 (reverse lst2))
; assigns first element lst1 to ele – again
;remember that were first/resting through the
list.
; will go through each element of lst1
; will return the reverse of lst2
(setq lst2 (cons ( * ele ele )
lst2)))))
(sq-list ‘(1 2 3 4)
 (1 4 9 16)

Another example:
(defun greet (people)
(dolist (person people)
(print (append ‘(so nice to see you)
(list person)))))
(setq guests ‘(sally bobo mr-potato-head))
(greet guests) ->
(so nice to see you sally)
(so nice to see you bobo)
(so nice to see you mr-potato-head)

The Do iteration
• The DO function
• (do ((<var1> [<init1> [<step1>]])
.
(<varn [<initn>[stepn>]]))
(<end-pred><fn>…..<fm> <retobj >)
body-exp1
body-exp2
[<body>]

Example of Do
Countdown
(defun iter (max)
(do ((num max (- num 1)))
; assign max to num ;
subtract 1 from num in loop
((<= num 0) ‘end) ;this is end cond.
(print num)))

Another example
Summing all integers
(defun iter-sum (max)
(do ((num max (- num 1))
(sum 0 (+ sum num)))
((<= num 0) sum)))
In English: Assign max to number, and 0 to sum.
Each time through the loop, subtract 1 from
num and add sum to number. When Number
is less than or equal to 0, return sum.

Yet Another
(defun reverse-list (ls)
(if (listp ls)
(do ((new-ls () (cons (first old-ls)
new-ls))
(old-ls ls (rest old-ls)))
((null old-ls) new-ls) ) ) )
[user]: (reverse-list '(3 1 4 1 5 9))
(9 5 1 4 1 3)
* With do function, you probably don’t
need a let --- because it’s in the do

Iterative Function
Write an iterative function called stop that takes
a list and a number and returns a list that does
not exceed the number

EVAL
• (EVAL <exp>) invokes the evaluation
procedure.
> (EVAL (LIST ‘+ 4 5))
9
> (SETQ A ‘X)
X
> (SETQ X 100)
100

Using EVAL to create fn at run-
time
Create a function to recognize cups based on the description
learned:
(defun create-cup-recognizer ( cup-description )
(eval (list ‘defun ‘cup-recognizer ‘( obj )
(list ‘subset
(list ‘quote cup-description)
‘obj)
) ) )
subset tests whether the first arg is a subset of the second arg.
> (create-cup-recognizer cup-def)
cup-recognizer

APPLY
• (APPLY <fn-name> <arg-list> )
>(APPLY ‘CONS ‘( A ( B C ) ) )
(A B C)
> (APPLY ‘CAR ‘( (A B C) ) )
A
> (SETQ OP ‘+)
+
> (APPLY OP ‘( 5 3 ) )
8

A Better Example of APPLY
Suppose we want to ``do something’’ to
an enemy identified, but the action is
determined at run time (based on their
actions, degree of threats, etc.).
(defun action (fn enemy)
(APPLY fn (cons enemy nil) ) )
(defun seize (person) .... )
(defun kill (person) ... )
(defun drunk (person) ... )

FUNCALL -- A sibling of APPLY
• (FUNCALL <fn-name> <arg1> <arg2> ...)
>(FUNCALL ‘CONS ‘A ‘( B C ) )
(A B C)
> (FUNCALL ‘CAR ‘(A B C) )
A
> (SETQ OP ‘+)
+
> (FUNCALL OP 5 3 )
8
> (SETQ OP ‘-)
-
> (FUNCALL OP 5 3 )

What if we want to apply a
function to an entire list?
(MAPCAR <fn-name> <arg1-list> <arg2-
list>)
> (MAPCAR ‘+ ‘(1 2 3 4) ‘(100 200 300
400) )
(101 202 303 404)
> (SETQ action ‘drunk)
DRUNK
> (SETQ hostile-guests (identify-enemy
guests))

LAMBDA -- An anonymous function
• (LAMBDA (<arg1> <arg2> ...) <exp1> ....)
• How to invoke it? It is typically invoked by
EVAL, APPLY, FUNCALL, or mapping
functions.
> (MAPCAR (lambda (x) (+ x bonus)) salary-
list )
• When to use it? It is typically used for
defining a function that is needed only in
a specific situation.

Understanding LAMBDA form
• Viewing it as a function with a name
you imagined.
( @@@@ .... (lambda .............. )
###... )
( @@@@ ... <imaginary-fn-name> ###
... )

From an MIT job advert
…Applicants must also have extensive
knowledge of C and UNIX, although they should
also have sufficiently good programming taste to
not consider this an achievement…

Why should I teach Lisp?
Lisp is the basis for the teaching of programming at MIT. Why?
– Lisp doesn’t force any single style. The same language can be used to teach
procedural, data-flow, and object-oriented programming.
– Students don’t have to learn a different syntax for each style of programming.
– Students learn how the different styles work, by implementing them in Lisp as
well as programming them.
– Lisp has a closeness to formal language theory that helps understanding of
compilers, interpreters, and language semantics.
Lisp’s success as a teaching language isn’t a coincidence. It has a unique
simplicity and mathematical elegance that corresponds to the concepts that
are taught in programming.

First-course programming language
frequencies
140 Pascal
57 Ada
48 Scheme
45 Modula (52 for all dialects)
25 C
8 Fortran
7 C++
6 Modula-2
5 SML
4 Turing
… …

Lisp: A language surrounded by
myths
Many things are said of Lisp that aren’t true.
– It is big.
– It is slow.
– It is difficult to learn.
– It is an “artificial intelligence” language.
– It is an academic/research language.
Twenty years ago, these were true, but Lisp has evolved, and the
requirements on programming languages have changed.
– Software costs outweigh hardware costs; Lisp’s fast development can
reduce costs significantly.
– Lisp has changed to meet new requirements; today it has as much
Pascal in its ancestry as it does traditional Lisp.

What is Lisp?
• “Lisp” is an acronym for “list processing”
• Derived from Church’s lambda calculus, but made efficiently
computable
• Originally designed for symbolic processing, eg. differentiation
and integration of algebraic expressions
• Data as symbolic expressions; simple, general data structures,
for both programs and data.
• Consistent; everything in Lisp is a first-class object, numbers,
lists, functions, symbols, objects, etc.
• Lisp has a thirty-odd year long history of success; it has a
culture, and has been proved many times over.

Lisp dialects today
There are two dialects in common use today, reflecting different
requirements
– Common Lisp;
• designed as an “industrial strength” language.
• powerful, expressive, and efficient.
– Scheme;
• designed as a teaching language, and in common use today.
• a small, clean “gem-like” language with Pascal in its ancestry.
These languages have many features in common.
– Both run on almost every kind of computer.
– Many public domain implementations exist.
– Both are designed to be compiled efficiently.
– Both use static binding, rather than the dynamic binding common in
interpreted implementations in the past.

Data typing in Lisp
Lisp is a “dynamically typed” language; it associates the type with the value
rather than the variable.
• First class data types include:
– Symbols; nil, factorial, +
– Cons cells, lists; (1 2 3 4 5), (foo . bar)
– Numbers; 1, 1.63552716, 2/3, 66159327278283638
– Functions; #’factorial, #’+
– Closures; #<lexical-closure #x62AF40>
– Objects; #<stack #x62AE20>
– Arrays; "Hello there", #(1 2 3 4 5)
– Hash tables; #<hash-table #x61AE90>
Detecting errors at run-time is easy; the debugger will appear whenever there
is a type error.

Is Lisp object-oriented?
The Common Lisp object system is perhaps the most advanced object system
in the world. It features:
– Multiple inheritance
– Multimethods; dispatching on more than one parameter
Class name Superclasses
Method name Parameters
Slot name
(defclass stack ()
((elements :initform ()
:accessor stack-elements)))
(defmethod clear-stack ((my-stack stack))
(setf (stack-elements my-stack) ()))

An example using CLOS
It is easy to define a stack class in CLOS:
(defclass stack ()
((elements :initform ()
:accessor stack-elements)))
(defmethod push-element ((my-stack stack) element)
(push element (stack-elements my-stack)))
(defmethod pop-element ((my-stack stack))
(check-type (stack-elements my-stack) cons)
(pop (stack-elements my-stack)))
defclass defines a class
defmethod defines a method
setf assignment
push/pop Common Lisp stack primitives

Beyond message-passing:
multimethods
In CLOS, methods can dispatch on more than one parameter; message-
passing is replaced by “generic functions”.
(defmethod append-list ((list1 list) (list2 list))
(append list1 list2))
(defmethod append-list (list1 (list2 stack))
(append-list list1 (stack-elements list2)))
(defmethod append-list ((list1 stack) list2)
(append-list (stack-elements list1) list2))
Calling (append-list stack1 stack2) calls the third method, which calls the
second method, which calls the first method, and then returns the final
result.

Cascades of languages
Lisp programs often end up as cascades of languages, starting
with general-purpose ones which gradually become more
specialised to the task.
This has several advantages, including, for example:
– Reuse
– Reliability; ease of debugging
Lisp
Constraint language
Scheduling language
Timetabling language
The application: Syllabus

Languages and Lisp
Lisp makes the treatment of different languages simple; this is the core of a
Lisp interpreter for Scheme.
(define (eval exp env)
(cond ((self-evaluating? exp) exp)
((quoted? exp) (text-of-quotation exp))
((variable? exp) (lookup-variable-value exp env))
((definition? exp) (eval-definition exp env))
((assignment? exp) (eval-assignment exp env))
((lambda? exp) (make-procedure exp env))
((conditional? exp) (eval-cond (clauses exp) env))
((application? exp)
(apply (eval (operator exp) env)
(list-of-values (operands exp) env)))
(else (error “Unknown expression” exp))))
Exploration of the underlying mechanism makes programs easier to
understand. Lisp is a natural for this.

New dimensions of Lisp:
macros and macro packages
Macros are Lisp programs that transform other Lisp programs, by operating
on them as data structures.
• General purpose macro packages can be developed.
– Series package; data-flow programming.
– Screamer package: nondeterministic programming.
• Normal definition for the factorial function
(defun fact (n)
(if (= n 1)
1
(* n (fact (- n 1)))))
• Series package definition for the factorial function
(defun fact (n)
(collect-product (scan-range :start 1 :upto n)))

Debugging in Lisp
Lisp has an interactive debugger. When an error happens, a window like this
appears, and the inspector can be used to help find the problem.

Expanding the business:
using Lisp in industry
• Many large companies have small groups using Lisp to solve
real problems, but the prejudices still live.
• A principal advantage of Lisp is its development speed; other
languages need more effort for the same result.
• Even with an incremental C++ environment, Lisp programmers
of the same skill are 2–5 times more productive; this makes
some projects viable which otherwise wouldn’t be.
• Skill shortage is a problem, but training is straightforward and
people quickly become productive.

Conclusions:
the “take home” message
• Lisp is the victim of pride and prejudice.
• Lisp is proven; it has adapted and survived through its thirty-
year history.
• Lisp can be—and is—used effectively as a teaching resource.
• Lisp encourages a style of cascading languages; ideal for
software reuse.
• Lisp can make—and has made—some “niche” applications
viable in industry.
• Lisp is still adapting to new circumstances and breaking new
ground. It’ll be here for many years yet.

Pride and prejudice:
Four decades of Lisp
Stuart Watt
Human Cognition Research Laboratory,
The Open University,
Walton Hall,
Milton Keynes, MK7 6AA.
Email: S.N.K.Watt@open.ac.uk
Presented to:
Workshop on the choice of programming languages
29-30th September 1993

If..then..else
IF <test> <then> <else>
(If (> n 1) (+ n 1) (* n n))
In English: If n is greater than 1 then add 1 to n,
else times n by itself.
You can also have just:
(If (> n 1) (+ n 1)) -> but it will return a nil
Try and avoid nested ifs….

Functions as Arguments
• EVAL
• APPLY
• Mapping Functions

Homework Problems
Problems:
1. Write a function (power 3 2) = 3^2 = 9
2. Write a function that counts the number of atoms in an expression.
(count-atoms '(a (b) c)) --> 3
3. (count-anywhere 'a '(a ((a) b) a)) --> 3
4. (dot-product '(10 20) '(3 4)) --> 10x3 + 20x4 = 110
5. Write a function (flatten '(a (b) () ((c)))) --> (a b c)
which removes all levels of parenthesis and returns a flat list of
atoms.
6. Write a function (remove-dups '(a 1 1 a b 2 b)) --> (a 1 b 2)
which removes all duplicate atoms from a flat list.
(Note: there is a built-in remove-duplicates in Common Lisp, do not
use it).

Solutions 1-3
(defun power (a b)
"compute a^b - (power 3 2) ==> 9"
(if (= b 0) 1
(* a (power a (- b 1)))))
(defun count-atoms (exp)
"count atoms in expresion - (count-atoms '(a (b) c)) ==> 3"
(cond ((null exp) 0)
((atom exp) 1)
(t (+ (count-atoms (first exp)) (count-atoms (rest exp))))))
(defun count-anywhere (a exp)
"count performances of a in expresion -
(count-anywhere 'a '(a ((a) b) (a))) ==> 3"
(cond ((null exp) 0)
((atom exp) (if (eq a exp) 1 0))
(t (+ (count-anywhere a (first exp))
(count-anywhere a (rest exp))))))

Solutions
(defun flatten (exp)
"removes all levels of paranthesis and returns flat list of
atomsi
(flatten '(a (b) () ((c)))) ==> (a b c)"
(cond ((null exp) nil)
((atom exp) (list exp))
(t (append (flatten (first exp))
(flatten (rest exp))))))

Homework
• VECTORPLUS X Y
: Write a function (VECTORPLUS X Y) which takes two lists
and adds each number at the same position of each list.
• Example
– (VECTORPLUS ‘(3 6 9 10 4) ‘(8 5 2))
→ (11 11 11 10 4)
Solution
(DEFUN VECPLUS (X Y)
(COND
((NULL X) Y)
((NULL Y) X)
(T (CONS (+ (CAR X) (CAR Y)
(VECPLUS (CDR X) (CDR Y))))))

For Homework
 Representing predicate calculus with LISP
 ∀x likes (x, ice_cream)
(Forall (VAR X) (likes (VAR X) ice_cream))
 ∃x foo (x, two, (plus two three))
(Exist (VAR X) (foo (VAR X) two (plus two three)))
 Connectives
 ¬S (nott S)
 S1 ∧ S2 (ANDD S1 S2)
 S1 ∨ S2 (ORR S1 S2)
 S1 → S2 (imply S1 S2)
 S1 ≡ S2 (equiv S1 S2)
 Do not use the builtin predicate names.

For Homework
• Examples of predicates
– true, false
– likes (george, kate) likes (x, george)
likes (george, susie) likes (x, x)
likes (george, sarah, tuesday)
friends (bill, richard)
friends (bill, george)
friends (father_of(david), father_of(andrew))
helps (bill, george)
helps (richard, bill)
equals (plus (two, three), five)

For Homework
(DEFUN atomic_s (Ex)
(cond ((member Ex ‘(true false)) T)
((member (car Ex) ‘(likes …))
(Test_term (cdr Ex)))
(T nil)))
(DEFUN Test_term (ex1)
(cond ((NULL ex1) T)
((member (car ex1) ‘(george …..)
(test_term (cdr ex1)))
((VARP (car ex1)) (test_term (cdr ex1)))
((functionp (car ex1)) (test_term (cdr ex1)))
(T nil)))

Exercises
Evaluate the following:
(setq x 'outside)
(let ((x 'inside) (y x))
(list x y))
(let* ((x 'inside) (y x))
(list x y))

INTRODUCTION TO LISP

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INTRODUCTION TO LISP