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TEACH SETS2                              A. Sloman and S. Hardy Oct 1980
                                           Revised Aaron Sloman Dec 1996

     PREDICATES - LISTS - SETS - LOGIC: Some Basic Procedural Ideas

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your answers.


 -- Introduction
 -- Predicates
 -- Exercises defining predicates
 -- Defining relations
 -- Defining issubset
 -- Checking whether a list is redundant
 -- Searching for an instance of a predicate: exists
 -- Using an anonymous procedure with exists
 -- Using Partial application (Optional topic)
 -- The findone exercise
 -- Defining all(list, pred) -> boolean
 -- Defining hasodd, haseven, allodd, alleven
 -- Extending findone to findall(list, pred) -> newlist
 -- Understanding secret
 -- Pruning redundant elements from a list
 -- Defining union(list1, list2) -> newlist
 -- Defining intersection
 -- Defining larger(list1, list2) -> boolean
 -- Defining more(list1, pred1, list2, pred2) -> boole;
 -- Defining most
 -- Defining removall and subtract
 -- Defining overlaps and excludes
 -- The subculture problem
 -- Towards an intelligent database

-- Introduction -------------------------------------------------------

Read TEACH SETS before working on this file. You should be familiar with
the syntax for defining procedures. See HELP * DEFINE for revision,
or the Pop-11 primer (chapter 4). TEACH STACK is also useful for
understanding how procedures communicate with one another.

Note that the syntax of Pop-11 has changed somewhat since this file was
first produced.

Examples have been modified to conform to the syntax of Pop-11 since
Poplog Version 15.01 (late 1995).

Some of these exercises are quite easy, others fairly advanced. They
will give you a lot of experience of list-processing, and using
"logical" concepts.

Questions 23 and 24 are quite difficult. They illustrate quite complex
programming tasks that are manageable in a language that includes list
processing facilities and automatic garbage collection but could be much
more difficult in other languages (e.g. Pascal, C, C++).

Some of the exercises have an optional section enclosed in double
square brackets [[ .... ]]. These sections presuppose an understanding
of "partial application". They can be ignored if you don't know about
partial application.

Partial application is a powerful technique for combining procedures
with data (which may be other procedures) to form new procedures. If you
want to learn about it look at:

    TEACH * PERCENT/closures,

or section 11.3 of the Barrett, Ramsay and Sloman book on Pop-11, or
the sections on closures and partial application in chapter 4 of
the Pop-11 Primer: TEACH PRIMER/'CHAPTER.4'

The next few exercises are rather trivial, but are designed to get you
used to some important terminology.

-- Predicates ---------------------------------------------------------

Here is an introduction to an important concept from logic.

A predicate is a procedure which produces the result TRUE or FALSE.
I.e. its result is a "boolean" object.

Standard predicates in POP11 include ISINTEGER, ISPROCEDURE, ATOM, ISWORD.

These are "recogniser" predicates can be applied to any object at all,
no matter what its type. Some predicates (like ODD and EVEN) defined
below can be applied only to a restricted class of objects (e.g.

Predicates are frequently used in IF or UNLESS expressions, e.g:



That is because the conditional expression that occurs before THEN
should evaluate to a result that is true or false.
    o If it is false then the instructions following THEN will not be
    o If it is true, or anything else other than false, then the
      instructions after THEN will be obeyed.

This means that in POP-11 any object can count as TRUE, as long as it
isn't the unique object FALSE.

Here is a predicate to decide whether a number is bigger than ten:

    define aboveten(number) -> boolean;
        number > 10 -> boolean

Notice that this produces a result, which is left on the stack. The
result is in fact the result of the operator ">", i.e. TRUE or FALSE.
The operator ">" leaves its result on the Pop-11 stack. It is then
assigned to the local variable boolean. However that is an output local
so when aboveten finishes running the value of boolean is put on the
stack. That is then the result of the whole procedure.

Test it

    aboveten(3) =>
    ** <false>
    aboveten(10) =>
    ** <false>
    aboveten(11) =>
    ** <true>

    aboveten("cat") =>
    ;;; INVOLVING:  cat 10
    ;;; DOING    :  > aboveten runproc


Some people prefer the functional style of programming where an output
local variable is not used, as here:

    define aboveten(number);
        number > 10

I.e. the result of the procedure is whatever the result is of the
    number > 10

-- Exercises defining predicates --------------------------------------

1. Define the following predicates:

    positive(number) -> boolean
        (TRUE iff its argument is greater than zero),

    negative(number) -> boolean
        (TRUE iff its argument is less than zero),

    even(number) -> boolean
        (TRUE iff its argument leaves a zero remainder when divided
        by two)


In defining the first two you can use the Pop-11 arithmetic infix
    > and <

For the last two you may find it helpful to use the infix operator
"mod", described in HELP * MOD. (You may also need the procedure NOT.)

2) What will be printed out by the following?

    (You should first decide what will be printed out -- preferably
    writing your answer down, and then check your answers using the
    computer to compile each line)

    isinteger(1), isinteger("a"), isinteger(1+5) =>

    isinteger(1.5), isinteger(1.0) =>

    isinteger([1]) =>

    islist([1]) =>

    isinteger('1'), isinteger(isinteger)=>

    isprocedure(isinteger), isprocedure(isprocedure) =>

    isprocedure(3), isprocedure("a")=>

    isword("cat") =>

    isword('cat') =>

    isword([cat]) =>

    islist([cat]) =>

    atom(3) =>

    atom(atom), atom(isinteger), atom("a"), atom([a b c])=>

-- Defining relations -------------------------------------------------

3) Questions (1) and (2) were about predicates of one argument. The
operations =, >=, <=, > and < (see POPSUMMARY or PRIMER) can be thought
of as predicates as they too produce the result TRUE or FALSE.

Predicates that take two or more arguments are sometimes called
"relations" or "relational predicates".

Sometimes they are called "binary predicates", "ternary predicates"
"N-ary predicates" etc. depending on whether they take two, three or N

We can define our own "relational predicates", e.g.

    define isless(numberone, numbertwo);
        numberone < numbertwo

You will notice that many of the relations already defined in POP11 have
names which are infix operations, which means that they can be executed
by putting their names between their arguments, as in

    X > 5

    Y >= X

Not all system predicates are like this, for example MEMBER.

As an exercise define a relation MUCHLESS of two arguments which returns
TRUE if its second argument exceeds its first by more than 100.

    define muchless(num1, num2) -> boole;

What should replace "..."

;;; Test it

    muchless(3, 2) =>
    muchless(3, 400) =>
    muchless(3, 2200) =>


4) Predicates can be applied to all sorts of things - not just numbers
as in most of the examples above.

We could, for example, define a procedure which given a 'thing' and a
list returns TRUE if the thing is in the list and FALSE otherwise. This
procedure, called MEMBER is already part of the Pop-11 system (at least
in Poplog).

TEACH SETS shows how you can define a similar procedure called

Try the procedure member on various pairs of arguments, e.g.

    vars cat = 99;

    member(cat,  [dog cat mouse]) =>

    member("cat", [dog cat mouse])=>

    member('cat', [dog cat mouse])=>

    member('cat', ['dog' 'cat' 'mouse'])=>

    member([cat], [dog cat mouse])=>

    member([cat], [[dog] [cat] [mouse]])=>

In each case try to predict the result, before you try it. If you get
the result wrong and don't know why, ask for help.

-- Defining issubset --------------------------------------------------

5) Use the procedure MEMBER to write a procedure called ISSUBSET which
takes two lists and returns TRUE if every element of the first list is
a MEMBER of the second, but FALSE otherwise. It should have this header.

    define issubset(list1, list2) -> boolean;

    issubset([2 4 3 1], [6 7 1 4 3 2 5])

should be TRUE but not

    issubset([e d], [a b c d])

HINT: use a loop in which you take each element of the first set, and
check whether it is a member of the second set. If you ever find one
that is not, return false. If you get through without finding one,
return true. Make sure that your definition of issubset (in your file
sets2.p) has proper comments explaining WHAT the procedure is supposed
to do (not HOW it does it), and including some tests in a comment after
the definition.

What would you expect the following to return?

     issubset([], [1 2 3 4])=>
     issubset([a e], [a b [e] d c])=>
     issubset("a", [a b c d])=>

Check your version on those examples.

-- Checking whether a list is redundant -------------------------------

6) If the following is true

    member(hd(list), tl(list))

then the first element of the list (hd(list)) must occur at least
twice in the list.

Can you use this fact to write a procedure, called REDUNDANT, which,
given a list, returns TRUE if there are any repeated elements, so that:

    ** false
     redundant([1 3 5 2 1])=>
    ** <true>

What about this?
    redundant([[a] [b] [a]])

Put your definition into your sets2.p file, preceded by a specification
of what it does, and followed by test examples.

-- Searching for an instance of a predicate: exists -------------------

7) What does the following procedure do? (Assume LIST is a list and
PRED a predicate.)

    define exists(list, pred) -> result;
        if  list == [] then
            false -> result
        elseif  pred(hd(list)) then
            true -> result
            exists(tl(list),pred) -> result

what will The following do (TRACE EXISTS for some of them):

    exists([a b 1 2 3 e] , isinteger)=>
    exists([a b 1 2 3 e] , isword)=>
    exists([a b 1 2 3 e] , isprocedure)=>
    exists([% "a", "double", "hd", tl %], isprocedure)=>
    exists([% "a", "double", "hd", "tl" %], isprocedure)=>

Try changing the definition of exists so that instead of returning TRUE
it returns the actual object found that satisfies the predicate, e.g.

    exists([a b 1 2 3 e] , isinteger)=>
    ** 1

    exists([a b 1 2 3 e] , isword)=>
    ** a

-- Using an anonymous procedure with exists ---------------------------

We can use PROCEDURE ..... END to create AN ANONYMOUS procedure to be
the second argument of EXISTS, if a suitable procedure has not
previously been defined, as in

    exists([a 1 b 2 c 3], procedure(x); member(x,[2 4 6 8]) endprocedure)=>

This checks whether there exists an element of [A 1 B 2 C 3] which is
also an element of [2 4 6 8].

You can also write the above as:

    exists([a 1 b 2 c 3],
        procedure(x); member(x,[2 4 6 8]) endprocedure)=>

(In Pop-11 line breaks are normally equivalent to spaces.)

-- Using Partial application (Optional topic) -------------------------

[[Ignore everything up to the next section header if you have not learnt
about partial application, or if you are finding it hard to keep up:

Alternatively, instead of using PROCEDURE to create a whole new
procedure, we can "partially apply MEMBER to a list to create a
procedure which tests if its argument is a member of the list, thus:

     exists([a 1 b 2 c 3], member(% [2 4 6 8] %))=>

    member(% [2 4 6 8] %)

is a new predicate created from member by partially applying it to the
list [2 4 6 8]. This sort of procedure is called a "closure" - in this
case a closure of member. See HELP * CLOSURES).


-- The findone exercise -----------------------------------------------

8) Using a method similar to the previous definition of exists, or
another method if you prefer, define a procedure called FINDONE which
takes a list and a predicate as arguments. If there is an element of the
list which satisfies the predicate then FINDONE should produce the first
such element as its result; if there isn't one the result should be
FALSE. thus:

    findone([a b 3 4 5], isprocedure)=>
    ** <false>

    findone([a b 3 4 5], isinteger)=>
    ** 3

    findone([a b ^sqrt 3 4 5 ^hd], isprocedure)=>
    ** <procedure sqrt>

See if there is an ATOM (i.e. a non-list) in some list thus:

     findone([[a b] [c d] [e]], atom) =>
    ** <false>

        [a b [a b] [c d] e],
        procedure(x); lvars x; not(atom(x)) endprocedure)=>
    ** [a b]

Instead of that complex anonymous procedure we can create a procedure
by composing atom and not using <> thus;

     findone([a b [a b] [c d] e], atom<>not) =>

-- Defining all(list, pred) -> boolean

9) Define a procedure ALL which, like EXISTS, takes two arguments LIST
and PRED and produces TRUE if every element of the LIST satisfies PRED
but FALSE if at least one element of LIST does not satisfy PRED.

    all([2 4 6 8 a], isinteger)=>
    ** <false>

    all([2 4 6 8], isinteger)=>
    ** <true>

        [a b c],
        procedure x; lvars x; member(x, [a 1 3 c 2 b]) endprocedure)=>
    ** <true>

[[Ignore the next two, if you don't know about partial application

    all([a b c], member(% [a 1 3 c 2 b] %))=>
    ** <true>

    all([a b c], member(% [d 1 3 e 2 f] %))=>
    ** <false>


What result should the following produce?

    all([], isprocedure) =>
    all([], isword) =>

It may help to be reminded of the ISSUBSET procedure from question 5.

-- Defining hasodd, haseven, allodd, alleven

10) Using the predicates ODD and EVEN (see question one) and the
procedure EXISTS (see question seven) define two predicates HASODD and
HASEVEN each of which takes a list and produces TRUE if at least one
element of the list is odd (for HASODD) or even (for HASEVEN).

Similarly, using the procedure ALL define two predicates ALLODD and
ALLEVEN such that:

    allodd([1 3 5 6 7])=>
    ** <false>
    allodd([1 3 5 7])=>
    ** <true>

-- Extending findone to findall(list, pred) -> newlist ----------------

11) Define a procedure FINDALL which takes a list LIST and a predicate
PRED and produces as a result a list (possibly []) containing all the
elements of LIST which satisfy PRED.

    findall([a 1 b 2 c 3], isinteger)

should produce the result [1 2 3]. Try to instantiate the following
schema, and test your procedure.

    define findall(list, pred) -> result;

       if   .... then
            ... -> result
       elseif  pred(hd(list)) then
            [^( ...) ^^(findall(... , ...))] -> result

-- Understanding secret -----------------------------------------------

12) What does the following procedure do?

    define secret(item, list) -> result;
       if  list == [] then
            [] -> result
       elseif  item = hd(list) then
            tl(list) -> result
            [ ^(hd(list)) ^^(secret(item,tl(list)))] -> result a

What will the following print? (Predict the answer then check it.)

     secret("b", [b])=>
     secret("b", [a b c])=>
     secret("b", [[a] [b] [c]])=>
     secret("b", [a b c d e])=>
     secret("b", [a b c b a])=>
     secret([b], [[a] [b] [c]])=>

There is a library procedure which behaves like SECRET, except that it
deletes all occurrences of ITEM in the list. It is called delete. Try
all these

     delete("b", [b])=>
     delete("b", [a b c])=>
     delete("b", [[a] [b] [c]])=>
     delete("b", [a b c d e])=>
     delete("b", [a b c b a])=>
     delete([b], [[a] [b] [c]])=>

Actually delete can be used in several different formats
See REF * delete

-- Pruning redundant elements from a list -----------------------------

13) Using the library procedure DELETE define a procedure PRUNE which
takes a list as argument and produces a list as its result. The new list
should contain the same elements as the original one, but with all
redundancies removed (compare question 6).

Thus prune([A B 1 2 C 2 1 B A]) should produce [A B 1 2 C].

The above definition is ambiguous. Can you explain why?

Can you make it more precise, so that the result should be [A B 1 2 C]
and not for example [C 2 1 B A] which also contains no repetitions?

-- Defining union(list1, list2) -> newlist ----------------------------

14) Using the procedure PRUNE (or the procedure DELETE) define a
procedure called UNION which takes two lists as arguments and produces a
new list which contains every element of the two arguments but with no
redundant elements, thus:

    union([a b c d], [e f c d])=>
    ** [a b e f c d]

Compare what happens if you reverse the order:
    union([e f c d], [a b c d])=>
    ** [e f a b c d]

To make sure that the union of two lists is uniquely defined by their
elements you could use the library procedure SORT to sort the list after
it has been pruned. This will only work on lists of words, or lists of
numbers. Then:

     union([a b c d], [e f c d])=>
     ** [a b c d e f ]

-- Defining intersection ----------------------------------------------

15) Define a procedure called INTERSECTION which takes two lists as
arguments and returns a list of all the items common to both, thus:

    intersection([a b c d], [e c f d])=>
    ** [c d]

-- Defining larger(list1, list2) -> boolean ---------------------------

16) Define a procedure called LARGER which takes two lists as arguments
and produces the result TRUE if the first has MORE distinct elements
than the second, otherwise FALSE, thus:

    larger([a a a], [b c])=>
    ** <false>

    larger([1 2 3], [a b a b])=>
    ** <true>

You may find it useful to define a procedure called SETSIZE in terms of
PRUNE and the system procedure LENGTH.

    setsize([a a a]) =>
    ** 1
    setsize([a b a b a c]) =>
    ** 3

-- Defining more(list1, pred1, list2, pred2) -> boole; ----------------

17) Define a procedure called MORE which takes four arguments, thus:

    define more(list1, pred1, list2, pred2) -> boole;
        lvars list1, pred1, list2, pred2, boole;

Where LIST1 and LIST2 are lists and PRED1 and PRED2 are predicates.

MORE is to return TRUE if the number of elements of LIST1 which satisfy
PRED1 is greater than the number of elements of LIST2 which satisfy
PRED2, otherise false. thus:

     vars lista = [1 2 3 a b];
     vars listb = [1 2 b c d];

     more(lista, isinteger, listb, isinteger)=>
     ** <true>

     more(listb, isword, lista, isinteger)=>
     ** <false>

     more(lista, isinteger, lista, isword)=>
     ** <true>

You will find it helpful to define a procedure HOWMANY(LIST, PREDICATE)
which counts the number of elements in LIST which satisfy PREDICATE. You
could use FINDALL, mentioned above, and LENGTH.

What do you think should be done if one of the lists has repeated

-- Defining most ------------------------------------------------------

18) Using MORE define a procedure, called MOST, which takes two
arguments - a list and a predicate - and returns TRUE iff MOST of the
elements of the list satisfy the predicate. Is there a better way to
define MOST than by using MORE?

WARNING: if you forget to declare some of your local variables as
lexically scoped using lvars, you may find that your procedures produce
very strange errors, including infinite recursion (or recursion stack

-- Defining removall and subtract -------------------------------------

19) Define a procedure called REMOVEALL which, when applied to a list
and a predicate produces a list containing all the elements of the list
which do NOT satisfy the predicate. Can you do this using FINDALL (see
question 11)?

20) Define a procedure called SUBTRACT which takes two lists, called say
LISTA and LISTB, and produces a new list of the elements of LISTA which
are not in LISTB. Thus:

    subtract([b c d e f g], [a b c d g])=>
    ** [e f]

[[If you know about partial application, you may find it helpful to
employ REMOVEALL and MEMBER, using the fact that MEMBER partially
applied to LISTA (i.e. MEMBER(%LISTA%)) is a predicate TRUE only for the
elements of LISTA.]]

-- Defining overlaps and excludes -------------------------------------

21) Define a procedure called OVERLAPS which takes two lists and
produces TRUE iff (i.e. if and only if) their INTERSECTION is non-empty.

22) Define a procedure EXCLUDES which takes two lists and returns TRUE
iff the two lists do not OVERLAP.

-- The subculture problem ---------------------------------------------

23) Suppose there are ten people in a town, their names being

    a b c d e f g h i j.

You are told that the following pairs of people talk to each other:

     [[a f] [f e] [g i] [h b] [c h] [j d] [g j] [b h] [d i]]

I.e. a and f talk to each other, f and e talk to each other, g and i
talk to each other, but a and b do not talk to each other.

We define a sub-culture as a set of people individuals who are
"connected" by talking. Thus information given to one member of a
subculture can flow to all the others.

Thus although i does not talk to j, i and j are connected via g. Some
connections may take more steps.

Clearly(!) a, e and f form a 'sub-culture' in this sense: information
given to any of them could get to any of the others, but not necessarily
to members of the other sub-cultures.

Define a procedure called SUBCULTURE which when given a list of two
element lists (like that above) groups the pairs together into larger
lists each of which is a maximal subculture, thus:

    subculture([[a b] [b c] [d e] [e f]]) =>
    ** [[a b c] [d e f]]

    subculture([[a e] [b f] [c g] [d g]]) =>
    ** [[a e] [b f] [c g d]]

Put your definition in the file sets2.p. Make sure you precede it with a
comment giving a specification of what the procedure does. Is the
specification given in this question sufficiently precise to cover all
cases? Does it determine the order in which the subcultures should be
listed? Does it determine the order in which the elements of each
subculture should be listed? Can you make your definition specify this?

Follow your definition with a comment including the above tests, and
more of your own.

Can you see any purpose for a program like this? What if the input were
a list of pairs of towns connected by railway lines? Or a list of pairs
of numbers with common factors?

Subculture finds maximal "cliques", i.e. maximal subsets of items
that occur in the same list.

-- Towards an intelligent database ------------------------------------

24) Suppose you were given the following information about a set of
individuals A, B, C, D etc.


For example, the properties might be things like FEMALE, UNMARRIED,

You are asked to make guesses about which properties can be wholly or
partially 'explained' in terms of the others.
For example:

    Is being OVER6FT a sufficient condition for being TALL?
    Is being UNMARRIED a sufficient condition for being a SPINSTER?

Think about, and if possible develop, a program which can make 'good'
guesses about the answers to questions like these.

For example, to refute the assertion that P1 is necessary for P2 find an
individual of which P2 is TRUE but not P1.

You may want to assume that an individual lacks any property not
explicitly ascribed to him or her.

You may find it useful also to look at some of the ideas in

Revision material is in TEACH * DEFINE, TEACH * STACK

--- $poplocal/local/teach/sets2
--- Copyright University of Birmingham 1996. All rights reserved. ------