What is Logic Programming

What is Logic Programming?

At the beginning of this course, we stressed that computer science deals with imperative (how to) knowledge, whereas mathematics deals with declarative (what is) knowledge. Indeed, programming languages require that the programmer express knowledge in a form that indicates the step-by-step methods for solving particular problems. On the other hand, high-level languages provide, as part of the language implementation, a substantial amount of methodological knowledge that frees the user from concern with numerous details of how a specified computation will progress.

Most programming languages, including Lisp, are organized around computing the values of mathematical functions. Expression-oriented languages (such as Lisp, Fortran, and Algol) capitalize on the "pun" that an expression that describes the value of a function may also be interpreted as a means of computing that value. Because of this, most programming languages are strongly biased toward unidirectional computations (computations with well-defined inputs and outputs). There are, however, radically different programming languages that relax this bias. Logic programming extends this idea by combining a relational vision of programming with a powerful kind of symbolic pattern matching called unification.

This approach, when it works, can be a very powerful way to write programs. Part of the power comes from the fact that a single "what is" fact can be used to solve a number of different problems that would have different "how to" components.

A Sample Database

Before we get into the specifics of logic programming, we need a database to play with. You can load this database using:

> (load "~cs61as/lib/query.scm")
> (initialize-data-base microshaft-data-base)
> (query-driver-loop)

The personnel data base for Microshaft contains assertions about company personnel. Here is the information about Ben Bitdiddle, the resident computer wizard:

(address (Bitdiddle Ben) (Slumerville (Ridge Road) 10))
(job (Bitdiddle Ben) (computer wizard))
(salary (Bitdiddle Ben) 60000)

Each assertion is a list (in this case a triple) whose elements can themselves be lists.

As resident wizard, Ben is in charge of the company's computer division, and he supervises two programmers and one technician. Here is the information about them:

(address (Hacker Alyssa P) (Cambridge (Mass Ave) 78))
(job (Hacker Alyssa P) (computer programmer))
(salary (Hacker Alyssa P) 40000)
(supervisor (Hacker Alyssa P) (Bitdiddle Ben))
(address (Fect Cy D) (Cambridge (Ames Street) 3))
(job (Fect Cy D) (computer programmer))
(salary (Fect Cy D) 35000)
(supervisor (Fect Cy D) (Bitdiddle Ben))
(address (Tweakit Lem E) (Boston (Bay State Road) 22))
(job (Tweakit Lem E) (computer technician))
(salary (Tweakit Lem E) 25000)
(supervisor (Tweakit Lem E) (Bitdiddle Ben))

There is also a programmer trainee, who is supervised by Alyssa:

(address (Reasoner Louis) (Slumerville (Pine Tree Road) 80))
(job (Reasoner Louis) (computer programmer trainee))
(salary (Reasoner Louis) 30000)
(supervisor (Reasoner Louis) (Hacker Alyssa P))

All of these people are in the computer division, as indicated by the word computer as the first item in their job descriptions.

Ben is a high-level employee. His supervisor is the company's big wheel himself:

(supervisor (Bitdiddle Ben) (Warbucks Oliver))
(address (Warbucks Oliver) (Swellesley (Top Heap Road)))
(job (Warbucks Oliver) (administration big wheel))
(salary (Warbucks Oliver) 150000)

Besides the computer division supervised by Ben, the company has an accounting division, consisting of a chief accountant and his assistant:

(address (Scrooge Eben) (Weston (Shady Lane) 10))
(job (Scrooge Eben) (accounting chief accountant))
(salary (Scrooge Eben) 75000)
(supervisor (Scrooge Eben) (Warbucks Oliver))
(address (Cratchet Robert) (Allston (N Harvard Street) 16))
(job (Cratchet Robert) (accounting scrivener))
(salary (Cratchet Robert) 18000)
(supervisor (Cratchet Robert) (Scrooge Eben))

There is also a secretary for the big wheel:

(address (Aull DeWitt) (Slumerville (Onion Square) 5))
(job (Aull DeWitt) (administration secretary))
(salary (Aull DeWitt) 25000)
(supervisor (Aull DeWitt) (Warbucks Oliver))

The data base also contains assertions about which kinds of jobs can be done by people holding other kinds of jobs. For instance, a computer wizard can do the jobs of both a computer programmer and a computer technician:

(can-do-job (computer wizard) (computer programmer))
(can-do-job (computer wizard) (computer technician))

A computer programmer could fill in for a trainee:

(can-do-job (computer programmer)
            (computer programmer trainee))

Also, as is well known,

(can-do-job (administration secretary)
            (administration big wheel))

Simple Queries

The query language allows users to retrieve information from the data base by posing queries in response to the system's prompt. For example, to find all computer programmers one can say

;;; Query input:
(job ?x (computer programmer))

The system will respond with the following items:

;;; Query results:
(job (Hacker Alyssa P) (computer programmer))
(job (Fect Cy D) (computer programmer))

The input query specifies that we are looking for entries in the data base that match a certain pattern. In this example, the pattern specifies entries consisting of three items, of which the first is the literal symbol job, the second can be anything, and the third is the literal list (computer programmer). The "anything" that can be the second item in the matching list is specified by a pattern variable, ?x. The general form of a pattern variable is a symbol, taken to be the name of the variable, preceded by a question mark. We will see below why it is useful to specify names for pattern variables rather than just putting ? into patterns to represent "anything". The system responds to a simple query by showing all entries in the data base that match the specified pattern.

A pattern can have more than one variable. For example, the query

(address ?x ?y)

will list all the employees' addresses.

A pattern can have no variables, in which case the query simply determines whether that pattern is an entry in the data base. If so, there will be one match; if not, there will be no matches.

The same pattern variable can appear more than once in a query, specifying that the same "anything" must appear in each position. This is why variables have names. For example,

(supervisor ?x ?x)

finds all people who supervise themselves (though there are no such assertions in our sample data base).

The query

(job ?x (computer ?type))

matches all job entries whose third item is a two-element list whose first item is computer:

(job (Bitdiddle Ben) (computer wizard))
(job (Hacker Alyssa P) (computer programmer))
(job (Fect Cy D) (computer programmer))
(job (Tweakit Lem E) (computer technician))

This same pattern does not match

(job (Reasoner Louis) (computer programmer trainee))

because the third item in the entry is a list of three elements, and the pattern's third item specifies that there should be two elements. If we wanted to change the pattern so that the third item could be any list beginning with computer, we could specify

(job ?x (computer . ?type))

For example,

(computer . ?type)

matches the data

(computer programmer trainee)

with ?type as the list (programmer trainee). It also matches the data

(computer programmer)

with ?type as the list (programmer), and matches the data


with ?type as the empty list ().

We can describe the query language's processing of simple queries as follows:

  • The system finds all assignments to variables in the query pattern that satisfy the pattern -- that is, all sets of values for the variables such that if the pattern variables are instantiated with (replaced by) the values, the result is in the data base.
  • The system responds to the query by listing all instantiations of the query pattern with the variable assignments that satisfy it.

Note that if the pattern has no variables, the query reduces to a determination of whether that pattern is in the data base. If so, the empty assignment, which assigns no values to variables, satisfies that pattern for that data base.

Assertions and Queries: Part 1

Add a couple assertions into the database about things that you like. This should look very similar to
(assert! (likes brian potstickers))
Next, write a query that returns all of the things you like. It should return to you all of the assertions you just added.

Assertions and Queries: Part 2

Add a few more assertions into the database about things that your project partner likes. Write another query that returns all of the things s/he likes.

Assertions and Queries: Part 3

Finally, write a query that will return all of the things that anoyone in the database likes.

Simple Queries

Give simple queries that retrieve the following information from the data base:
  1. all people supervised by Ben Bitdiddle;
  2. the names and jobs of all people in the accounting division;
  3. the names and addresses of all people who live in Slumerville.
Remember, to load the example database and run the query system, type the following commands into an interpreter:
 (load "~cs61as/lib/query.scm")
(initialize-data-base microshaft-data-base)

Compound Queries

Simple queries form the primitive operations of the query language. In order to form compound operations, the query language provides means of combination. One thing that makes the query language a logic programming language is that the means of combination mirror the means of combination used in forming logical expressions: and, or, and not. (Here and, or, and not are not the Lisp primitives, but rather operations built into the query language.)

We can use and as follows to find the addresses of all the computer programmers:

(and (job ?person (computer programmer))
     (address ?person ?where))

The resulting output is

(and (job (Hacker Alyssa P) (computer programmer))
     (address (Hacker Alyssa P) (Cambridge (Mass Ave) 78)))
(and (job (Fect Cy D) (computer programmer))
     (address (Fect Cy D) (Cambridge (Ames Street) 3)))

In general,

(and <query1> <query2> ... <queryn>)

is satisfied by all sets of values for the pattern variables that simultaneously satisfy <query1> <query2> ... <queryn>

As for simple queries, the system processes a compound query by finding all assignments to the pattern variables that satisfy the query, then displaying instantiations of the query with those values.

Another means of constructing compound queries is through or. For example,

(or (supervisor ?x (Bitdiddle Ben))
    (supervisor ?x (Hacker Alyssa P)))

will find all employees supervised by Ben Bitdiddle or Alyssa P. Hacker:

(or (supervisor (Hacker Alyssa P) (Bitdiddle Ben))
    (supervisor (Hacker Alyssa P) (Hacker Alyssa P)))

(or (supervisor (Fect Cy D) (Bitdiddle Ben)) 
    (supervisor (Fect Cy D) (Hacker Alyssa P)))

(or (supervisor (Tweakit Lem E) (Bitdiddle Ben))     
    (supervisor (Tweakit Lem E) (Hacker Alyssa P))) 

(or (supervisor (Reasoner Louis) (Bitdiddle Ben)) 
    (supervisor (Reasoner Louis) (Hacker Alyssa P)))

In general,

(or <query1> <query2> ... <queryn> )

is satisfied by all sets of values for the pattern variables that satisfy at least one of <query1> <query2> ... <queryn>.

Compound queries can also be formed with not. For example,

(and (supervisor ?x (Bitdiddle Ben))
     (not (job ?x (computer programmer))))

finds all people supervised by Ben Bitdiddle who are not computer programmers. In general,

(not <query1>)

is satisfied by all assignments to the pattern variables that do not satisfy <query1>.

The final combining form is called lisp-value. When lisp-value is the first element of a pattern, it specifies that the next element is a Lisp predicate to be applied to the rest of the (instantiated) elements as arguments. In general,

(lisp-value <predicate> <arg1> ... <argn>)

will be satisfied by assignments to the pattern variables for which the <predicate> applied to the instantiated <arg1> ... <argn> is true. For example, to find all people whose salary is greater than $30,000 we could write

(and (salary ?person ?amount)
     (lisp-value > ?amount 30000))


As long as we just tell the system isolated facts, we can’t get extraordinarily interesting replies. But we can also tell it rules that allow it to infer one fact from another. For example, if we have a lot of facts like:

(mother Eve Cain)

then we can establish a rule about grandmotherhood:

(assert! (rule (grandmother ?elder ?younger)
               (and (mother ?elder ?mom)
                    (mother ?mom ?younger) ))))

The rule says that the first part (the conclusion) is true if we can find values for the variables such that the second part (the condition) is true.

Again, resist the temptation to try to do composition of functions!

(assert! (rule (grandmother ?elder ?younger) ;; WRONG!!!!
               (mother ?elder (mother ?younger)) ))

Mother isn’t a function, and you can’t ask for the mother of someone as this incorrect example tries to do. Instead, as in the correct version above, you have to establish a variable (?mom) that has a value that satisfies the two motherhood relationships we need.

In this language the words assert!, rule, and, or, and not have special meanings. Everything else is just a word that can be part of assertions or rules.

Analyzing the Family Tree

Let's try writing some rules! The following database (see Genesis 4) traces the genealogy of the descendants of Ada back to Adam, by way of Cain:
(son Adam Cain)
(son Cain Enoch)
(son Enoch Irad)
(son Irad Mehujael)
(son Mehujael Methushael)
(son Methushael Lamech)
(wife Lamech Ada)
(son Ada Jabal)
(son Ada Jubal)
Formulate rules such as "If S is the son of F, and F is the son of G, then S is the grandson of G" and "If W is the wife of M, and S is the son of W, then S is the son of M" (which was supposedly more true in biblical times than today) that will enable the query system to find the grandson of Cain; the sons of Lamech; the grandsons of Methushael.

More Rules

Here's a slightly more complicated rule:

(rule (lives-near ?person-1 ?person-2)
      (and (address ?person-1 (?town . ?rest-1))
           (address ?person-2 (?town . ?rest-2))
           (not (same ?person-1 ?person-2))))

It specifies that two people live near each other if they live in the same town. The final not clause prevents the rule from saying that all people live near themselves. The same relation is defined by the very simple rule:

(rule (same ?x ?x))
Carpooling Time

By giving the query
(lives-near ?person (Hacker Alyssa P))
Alyssa P. Hacker is able to find people who live near her, with whom she can ride to work. On the other hand, when she tries to find all pairs of people who live near each other by querying
(lives-near ?person-1 ?person-2)
she notices that each pair of people who live near each other is listed twice; for example,
(lives-near (Hacker Alyssa P) (Fect Cy D))
(lives-near (Fect Cy D) (Hacker Alyssa P))
Why does this happen? Is there a way to find a list of people who live near each other, in which each pair appears only once? Explain. (Don't write the code for this!)

Logic as Programs

We can regard a rule as a kind of logical implication: If an assignment of values to pattern variables satisfies the body, then it satisfies the conclusion. Consequently, we can regard the query language as having the ability to perform logical deductions based upon the rules. As an example, consider the append operation. Append can be characterized by the following two rules:

  • For any list y, the empty list and y append to form y.
  • For any u, v, y, and z, (cons u v) and y append to form (cons u z) if v and y append to form z.

To express this in our query language, we define two rules for a relation

(append x y z)

which we can interpret to mean "x and y append to form z":

(assert! (rule (append () ?y ?y)))
(assert! (rule (append (?u . ?v) ?y (?u . ?z))
         (append ?v ?y ?z)))

The first rule has no body, which means that the conclusion holds for any value of ?y. Note how the second rule makes use of dotted-tail notation to name the car and cdr of a list.

Given these two rules, we can formulate queries that compute the append of two lists:

;;; Query input:
(append (a b) (c d) ?what)
;;; Query results:
(append (a b) (c d) (a b c d))

What is more striking, we can use the same rules to ask the question "Which list, when appended to (a b), yields(a b c d)?" This is done as follows:

;;; Query input:
(append (a b) ?what (a b c d))
;;; Query results:
(append (a b) (c d) (a b c d))

The new thing in logic programming is that we can run a "function" backwards! We can tell it the answer and get back the question. But the real magic is...

;;; Query input:
(append ?this ?that (a b c d))
;;; Query results:
(append () (a b c d) (a b c d))
(append (a) (b c d) (a b c d))
(append (a b) (c d) (a b c d))
(append (a b c) (d) (a b c d))
(append (a b c d) () (a b c d))

we can also ask for all pairs of lists that append to form (a b c d)! We can use logic programming to compute multiple answers to the same question! Somehow it found all the possible combinations of values that would make our query true.

How does the append program work? Compare it to the Scheme append:

(define (append a b)
    (if (null? a)
        (cons (car a) (append (cdr a) b)) ))

Like the Scheme program, the logic program has two cases: There is a base case in which the first argument is empty. In that case the combined list is the same as the second appended list. And there is a recursive case in which we divide the first appended list into its car and its cdr. We reduce the given problem into a problem about appending (cdr a) to b. The logic program is different in form, but it says the same thing.

(Just as, in the grandmother example, we had to give the mother a name instead of using a function call, here we have to give(car a) a name--we call it ?u.)

Word of Caution

The query system may seem to exhibit quite a bit of intelligence in using the rules to deduce the answers to the queries above. Actually, as we will see in the next section, the system is following a well-determined algorithm in unraveling the rules. Unfortunately, although the system works impressively in the append case, the general methods may break down in more complex cases.

The "working backward" magic used in the append case doesn't always work. Let's look at the following example, which reverses a list.

(assert! (rule (reverse (?a . ?x) ?y)
               (and (reverse ?x ?z)
                    (append ?z (?a) ?y) )))

(assert! (reverse () ()))

This works for (reverse (a b c) ?what) but not the other way around; it gets into an infinite loop. We can also write a version that works only backwards:

(assert! (rule (backward (?a . ?x) ?y)
               (and (append ?z (?a) ?y)
                    (backward ?x ?z) )))

(assert! (backward () ()))

But it's much harder to write one that works both ways. Even as we speak, logic programming fans are trying to push the limits of the idea, but right now, you still have to understand something about the below-the-line algorithm to be confident that your logic program won't loop.


Define rules to implement the last-pair operation of SICP exercise 2.17, which returns a list containing the last element of a nonempty list. Check your rules on queries such as (last-pair (3) ?x), (last-pair (1 2 3) ?x), and (last-pair (2 ?x) (3)). Do your rules work correctly on queries such as (last-pair ?x (3))?


Here are some takeaways from this subsection:

  • In logic programming, we assert facts and ask questions.
  • An assertion is represented by a list.
  • We use the query language to retrieve information from the data base.
  • Rules allow infering one fact from another.
  • We can write programs such as append with logic programming!

What's Next?

Go to the next subsection and learn how the query system works!