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Prolog is a general purpose logic programming language associated with artificial intelligence and computational linguistics.

Prolog has its roots in formal logic, and unlike many other programming languages, Prolog is declarative: The program logic is expressed in terms of relations, represented as facts and rules. Execution is triggered by running queries over these relations.

The language was first conceived by a group around Alain Colmerauer in Marseillemarker, Francemarker, in the early 1970s and the first Prolog system was developed in 1972 by Alain Colmerauer and Phillipe Roussel.

Prolog was one of the first logic programming languages, and remains among the most popular such languages today, with many free and commercial implementations available. While initially aimed at natural language processing, the language has since then stretched far into other areas like theorem proving, expert systems, games, automated answering systems, ontologies and sophisticated control systems. Modern Prolog environments support the creation of graphical user interfaces, as well as administrative and networked applications.


The name Prolog was chosen by Philippe Roussel as an abbreviation for (French for programming in logic). It was created around 1972 by Alain Colmerauer with Philippe Roussel, based on Robert Kowalski's procedural interpretation of Horn clauses. It was motivated in part by the desire to reconcile the use of logic as a declarative knowledge representation language with the procedural representation of knowledge that was popular in North America in the late 1960s and early 1970s. According to Robert Kowalski, the first Prolog system was developed in 1972 by Alain Colmerauer and Phillipe Roussel. The first implementations of Prolog were interpreters, however, David H. D. Warren created the Warren Abstract Machine, an early and influential Prolog compiler which came to define the "Edinburgh Prolog" dialect which served as the basis for the syntax of most modern implementations.

Much of the modern development of Prolog came from the impetus of the fifth generation computer systems project (FGCS), which developed a variant of Prolog named Kernel Language for its first operating system.

Pure Prolog was originally restricted to the use of a resolution theorem prover with Horn clauses of the form:

H :- B1, ..., Bn.

The application of the theorem-prover treats such clauses as procedures:

to show/solve H, show/solve B1 and ... and Bn.

Pure Prolog was soon extended, however, to include negation as failure, in which negative conditions of the form not(Bi) are shown by trying and failing to solve the corresponding positive conditions Bi.

Subsequent extensions of Prolog by the original team introduced Constraint logic programming abilities into the implementations.

Programming in Prolog

In Prolog, program logic is expressed in terms of relations, and execution is triggered by running queries over these relations. Relations and queries are constructed using Prolog's single data type, the term. Relations are defined by clauses. Given a query, the Prolog engine attempts to find a resolution refutation of the negated query. If the negated query can be refuted, i.e., an instantiation for all free variables is found that makes the union of clauses and the singleton set consisting of the negated query false, it follows that the original query, with the found instantiation applied, is a logical consequence of the program. This makes Prolog (and other logic programming languages) particularly useful for database, symbolic mathematics, and language parsing applications. Because Prolog allows impure predicates, checking the truth value of certain special predicates may have some deliberate side effect, such as printing a value to the screen. This permits the programmer to use some amount of conventional imperative programming when the logical paradigm is inconvenient. It has a purely logical subset, called "pure Prolog", as well as a number of extralogical features.

Data types

Prolog's single data type is the term. Terms are either atoms, numbers, variables or compound terms.

  • An atom is a general-purpose name with no inherent meaning. Examples of atoms include x, blue, 'Taco', and 'some atom'.
  • Numbers can be floats or integers.
  • Variables are denoted by a string consisting of letters, numbers and underscore characters, and beginning with an upper-case letter or underscore. Variables closely resemble variables in logic in that they are placeholders for arbitrary terms.
  • A compound term is composed of an atom called a "functor" and a number of "arguments", which are again terms. Compound terms are ordinarily written as a functor followed by a comma-separated list of argument terms, which is contained in parentheses. The number of arguments is called the term's arity. An atom can be regarded as a compound term with arity zero. Examples of compound terms are truck_year('Mazda', 1986) and 'Person_Friends'(zelda,[tom,jim]).

Special cases of compound terms:

  • A List is an ordered collection of terms. It is denoted by square brackets with the terms separated by commas or in the case of the empty list, []. For example [1,2,3] or [red,green,blue].
  • Strings: A sequence of characters surrounded by quotes is equivalent to a list of (numeric) character codes, generally in the local character encoding or Unicode if the system supports Unicode. For example, "to be, or not to be".

Rules and facts

Prolog programs describe relations, defined by means of clauses. Pure Prolog is restricted to Horn clauses, a Turing-complete subset of first-order predicate logic. There are two types of clauses: Facts and rules. A rule is of the form

Head :- Body.

and is read as "Head is true if Body is true". A rule's body consists of calls to predicates, which are called the rule's goals. The built-in predicate ,/2 (meaning a 2-arity operator with name ,) denotes conjunction of goals, and ;/2 denotes disjunction. Conjunctions and disjunctions can only appear in the body, not in the head of a rule.

Clauses with empty bodies are called facts. An example of a fact is:


which is equivalent to the rule:

cat(tom) :- true.

The built-in predicate true/0 is always true.

Given the above fact, one can ask:

is tom a cat?

?- cat(tom).

what things are cats?

?- cat(X).
X = tom

Due to the relational nature of many built-in predicates, they can typically be used in several directions. For example, length/2 can be used to determine the length of a list (length(List, L), given a list List) as well as to generate a list skeleton of a given length (length(X, 5)), and also to generate both list skeletons and their lengths together (length(X, L)). Similarly, append/3 can be used both to append two lists (append(ListA, ListB, X) given lists ListA and ListB) as well as to split a given list into parts (append(X, Y, List), given a list List). For this reason, a comparatively small set of library predicates suffices for many Prolog programs. All predicates can also be used to perform unit tests: Queries can be embedded in programs and allow for automatic compile-time regression testing.

As a general purpose language, Prolog also provides various built-in predicates to perform routine activities like input/output, using graphics and otherwise communicating with the operating system. These predicates are not given a relational meaning and are only useful for the side-effects they exhibit on the system. For example, the predicate write/1 displays a term on the screen.


Execution of a Prolog program is initiated by the user's posting of a single goal, called the query. Logically, the Prolog engine tries to find a resolution refutation of the negated query. The resolution method used by Prolog is called SLD resolution. If the negated query can be refuted, it follows that the query, with the appropriate variable bindings in place, is a logical consequence of the program. In that case, all generated variable bindings are reported to the user, and the query is said to have succeeded. Operationally, Prolog's execution strategy can be thought of as a generalization of function calls in other languages, one difference being that multiple clause heads can match a given call. In that case, the system creates a choice-point, unifies the goal with the clause head of the first alternative, and continues with the goals of that first alternative. If any goal fails in the course of executing the program, all variable bindings that were made since the most recent choice-point was created are undone, and execution continues with the next alternative of that choice-point. This execution strategy is called chronological backtracking. For example:

mother_child(trude, sally).

father_child(tom, sally).
father_child(tom, erica).
father_child(mike, tom).

sibling(X, Y)      :- parent_child(Z, X), parent_child(Z, Y).

parent_child(X, Y) :- father_child(X, Y).
parent_child(X, Y) :- mother_child(X, Y).

This results in the following query being evaluated as true:

?- sibling(sally, erica).

This is obtained as follows: Initially, the only matching clause-head for the query sibling(sally, erica) is the first one, so proving the query is equivalent to proving the body of that clause with the appropriate variable bindings in place, i.e., the conjunction (parent_child(Z,sally), parent_child(Z,erica)). The next goal to be proved is the leftmost one of this conjunction, i.e., parent_child(Z, sally). Two clause heads match this goal. The system creates a choice-point and tries the first alternative, whose body is father_child(Z, sally). This goal can be proved using the fact father_child(tom, sally), so the binding Z = tom is generated, and the next goal to be proved is the second part of the above conjunction: parent_child(tom, erica). Again, this can be proved by the corresponding fact. Since all goals could be proved, the query succeeds. Since the query contained no variables, no bindings are reported to the user. A query with variables, like:

?- father_child(Father, Child).

enumerates all valid answers on backtracking.

Notice that with the code as stated above, the query ?- sibling(sally, sally). also succeeds. One would insert additional goals to describe the relevant restrictions, if desired.

Loops and recursion

Iterative algorithms can be implemented by means of recursive predicates. Prolog systems typically implement a well-known optimization technique called tail call optimization (TCO) for deterministic predicates exhibiting tail recursion or, more generally, tail calls: A clause's stack frame is discarded before performing a call in a tail position. Therefore, deterministic tail-recursive predicates are executed with constant stack space, like loops in other languages.


The built-in Prolog predicate \+/1 provides negation as failure, which allows for non-monotonic reasoning. The goal \+ illegal(X) in the rule

legal(X) :- \+ illegal(X).

is evaluated as follows: Prolog attempts to prove the illegal(X). If a proof for that goal can be found, the original goal (i.e., \+ illegal(X)) fails. If no proof can be found, the original goal succeeds. Therefore, the \+/1 prefix operator is called the "not provable" operator, since the query ?- \+ Goal. succeeds if Goal is not provable. This kind of negation is sound if its argument is "ground" (i.e. contains no variables). Soundness is lost if the argument contains variables. In particular, the query ?- legal(X). can now not be used to enumerate all things that are legal.


There is a special notation called definite clause grammars (DCG). A rule defined via -->/2 instead of :-/2 is expanded by the preprocessor (expand_term/2, a facility analogous to macros in other languages) according to a few straightforward rewriting rules, resulting in ordinary Prolog clauses. Most notably, the rewriting equips the predicate with two additional arguments, which can be used to implicitly thread state around, analogous to monads in other languages. DCGs are often used to write parsers or list generators, as they also provide a convenient interface to list differences.

Parser example

A larger example will show the potential of using Prolog in parsing.

Given the sentence expressed in Backus-Naur Form:

   ::=   |  
   ::=   =  ;
  ::=   |   
     ::=   | 
          ::=  a | b
       ::=  0..9
    ::=  + | - | *

This can be written in Prolog using DCGs, corresponding to a predictive parser with one token look-ahead:

sentence(S)                --> statement(S0), sentence_r(S0, S).
sentence_r(S, S)           --> [].
sentence_r(S0, seq(S0, S)) --> statement(S1), sentence_r(S1, S).

statement(assign(Id,E)) --> id(Id), [=], expression(E), [;].

expression(E) --> term(T), expression_r(T, E).
expression_r(E, E)  --> [].
expression_r(E0, E) --> [+], term(T), expression_r(plus(E0,T), E).
expression_r(E0, E) --> [-], term(T), expression_r(minus(E0, T), E).

term(T)       --> factor(F), term_r(F, T).
term_r(T, T)  --> [].
term_r(T0, T) --> [*], factor(F), term_r(times(T0, F), T).

factor(id(ID))   --> id(ID).
factor(digit(D)) --> [D], { (number(D) ; var(D)), between(0, 9, D)}.

id(a) --> [a].
id(b) --> [b].

This code defines a relation between a sentence (given as a list of tokens) and its abstract syntax tree (AST). Example query:

?- phrase(sentence(AST), [a,=,1,+,3,*,b,;,b,=,0,;]).
AST = seq(assign(a, plus(digit(1), times(digit(3), id(b)))), assign(b, digit(0))) ;

The AST is represented using Prolog terms and can be used to apply optimizations, to compile such expressions to machine-code, or to directly interpret such statements. As is typical for the relational nature of predicates, these definitions can be used both to parse and generate sentences, and also to check whether a given tree corresponds to a given list of tokens. Using iterative deepening for fair enumeration, each arbitrary but fixed sentence and its corresponding AST will be generated eventually:

?- length(Tokens, _), phrase(sentence(AST), Tokens).
 Tokens = [a, =, a, (;)], AST = assign(a, id(a)) ;
 Tokens = [a, =, b, (;)], AST = assign(a, id(b))

Higher-order programming

First-order logic does not allow quantification over predicates. A higher-order predicate is a predicate that takes one or more other predicates as arguments. Since arbitrary Prolog goals can be constructed and evaluated at run-time, it is easy to write higher-order predicates like maplist/2, which applies an arbitrary predicate to each member of a given list, and sublist/3, which filters elements that satisfy a given predicate, also allowing for currying.

To convert solutions from temporal representation (answer substitutions on backtracking) to spatial representation (terms), Prolog has various all-solutions predicates that collect all answer substitutions of a given query in a list. This can be used for list comprehension. For example, perfect numbers equal the sum of their proper divisors:

perfect(N) :-
    between(1, inf, N), U is N // 2,
    findall(D, (between(1,U,D), N mod D =:= 0), Ds),
    sumlist(Ds, N).

This can be used to enumerate perfect numbers, and also to check whether a number is perfect.

Meta-interpreters and reflection

Prolog is a homoiconic language and provides many facilities for reflection. Its implicit execution strategy makes it possible to write a concise meta-circular evaluator (also called meta-interpreter) for pure Prolog code. Since Prolog programs are themselves sequences of Prolog terms (:-/2 is an infix operator) that are easily read and inspected using built-in mechanisms (like read/1), it is easy to write customized interpreters that augment Prolog with domain-specific features.


ISO Prolog

The ISO Prolog standard: ISO/IEC 13211-1 published in 1995, aims to standardise the existing practices of the many implementations of the core elements of Prolog. It has clarified aspects of the language that were previously ambiguous and leads to portable programs. The standard is maintained by the ISO X3J17 committee. The standard does not cover modules, libraries or DCGs.


For efficiency, Prolog code is typically compiled to abstract machine code, often influenced by the register-based Warren Abstract Machine (WAM) instruction set. Some implementations employ abstract interpretation to derive type and mode information of predicates at compile time, or compile to real machine code for high performance. Devising efficient implementation techniques for Prolog code is a field of active research in the logic programming community, and various other execution techniques are employed in some implementations. These include clause binarization and stack-based virtual machines.

The Prolog subset of Poplog is implemented using the extendable incremental compiler of POP-11, the core language of Poplog, which is a general purpose Lisp-like language with a more conventional syntax. The implementation required the Poplog Virtual Machine to be extended to provide support for Prolog continuations, Prolog variables, the Prolog trail (recording undoable variable bindings), and Prolog terms.The implementation was constrained by the need to allow data-structures to be shared with the other Poplog languages, especially POP-11 and Common Lisp, thereby providing support for a mixture of programming styles.

Some Prolog systems, like BProlog and XSB, implement an extension called tabling, which frees the user from manually storing intermediate results.

During the Fifth Generation Computer Systems project, there were attempts to implement Prolog in hardware with the aim of achieving faster execution with dedicated architectures. Furthermore, Prolog has a number of properties that may allow speed-up through parallel execution. A more recent approach has been to compile restricted Prolog programs to a field programmable gate array. However, rapid progress in general-purpose hardware has consistently overtaken more specialised architectures.


Here follow some example programs written in Prolog.

Hello world

An example of a query:?- write('Hello world!'), nl.Hello world!true.



The QuickSort sorting algorithm, relating a list to its sorted version:partition([], _, [], []).partition([X|Xs], Pivot, Smalls, Bigs) :-
   (   X @ Pivot ->
       Smalls = [X|Rest],
       partition(Xs, Pivot, Rest, Bigs)
   ;   Bigs = [X|Rest],
       partition(Xs, Pivot, Smalls, Rest)

quicksort([]) --> [].quicksort([X|Xs]) -->
   { partition(Xs, X, Smaller, Bigger) },
   quicksort(Smaller), [X], quicksort(Bigger).

Turing machine

Turing completeness of Prolog can be shown by using it to simulate a Turing machine:turing(Tape0, Tape) :-
   perform(q0, [], Ls, Tape0, Rs),
   reverse(Ls, Ls1),
   append(Ls1, Rs, Tape).

perform(qf, Ls, Ls, Rs, Rs) :- !.perform(Q0, Ls0, Ls, Rs0, Rs) :-
   symbol(Rs0, Sym, RsRest),
   once(rule(Q0, Sym, Q1, NewSym, Action)),
   action(Action, Ls0, Ls1, [NewSym|RsRest], Rs1),
   perform(Q1, Ls1, Ls, Rs1, Rs).

symbol([], b, []).symbol([Sym|Rs], Sym, Rs).

action(left, Ls0, Ls, Rs0, Rs) :- left(Ls0, Ls, Rs0, Rs).action(stay, Ls, Ls, Rs, Rs).action(right, Ls0, [Sym|Ls0], [Sym|Rs], Rs).

left([], [], Rs0, [b|Rs0]).left([L|Ls], Ls, Rs, [L|Rs]).

A simple example Turing machine is specified by the facts:rule(q0, 1, q0, 1, right).rule(q0, b, qf, 1, stay).

This machine performs incrementation by one of a number in unary encoding: It loops over any number of "1" cells and appends an additional "1" at the end. Example query and result:?- turing([1,1,1], Ts).Ts = [1, 1, 1, 1] ;

This example illustrates how any computation can be expressed declaratively as a sequence of state transitions, implemented in Prolog as a relation between successive states of interest. As another example for this, an optimizing compiler with three optimization passes could be implemented as a relation between an initial program and its optimized form:

program_optimized(Prog0, Prog) :-
   optimization_pass_1(Prog0, Prog1),
   optimization_pass_2(Prog1, Prog2),
   optimization_pass_3(Prog2, Prog).

or equivalently using DCG notation:

program_optimized --> optimization_pass_1, optimization_pass_2, optimization_pass_3.

Dynamic programming

The following Prolog program uses dynamic programming to find the longest common subsequence of two lists in polynomial time. The clause database is used for memoization:
- dynamic(stored/1).

memo(Goal) :- ( stored(Goal) -> true ; Goal, assertz(stored(Goal)) ).

lcs([], _, []) :- !.lcs(_, [], []) :- !.lcs([X|Xs], [X|Ys], [X|Ls]) :- !, memo(lcs(Xs, Ys, Ls)).lcs([X|Xs], [Y|Ys], Ls) :-
   memo(lcs([X|Xs], Ys, Ls1)), memo(lcs(Xs, [Y|Ys], Ls2)),
   length(Ls1, L1), length(Ls2, L2),
   (   L1 >= L2 -> Ls = Ls1 ; Ls = Ls2 ).

Example query:?- lcs([x,m,j,y,a,u,z], [m,z,j,a,w,x,u], Ls).Ls = [m, j, a, u]


Although Prolog is widely used in research and education, Prolog and other logic programming languages have not had a significant impact on the computer industry in general. Most applications are small by industrial standards with few exceeding 100,000 lines of code.Software developed in Prolog has been criticised for having a high performance penalty, but advances in implementation have made some of these arguments obsolete.


Various implementations have been developed from Prolog to extend logic programming capabilities in numerous directions. These include constraint logic programming (CLP), object-oriented logic programming (OOLP), concurrency, Linear Logic (LLP), functional and higher-order logic programming capabilities, plus interoperability with knowledge bases:

  • Constraint logic programming is important for many Prolog applications in industrial settings, like time tabling and other scheduling tasks. Most Prolog systems ship with at least one constraint solver for finite domains, and often also with solvers for other domains like rational numbers.
  • HiLog and λProlog extend Prolog with higher-order programming features.
  • F-logic extends Prolog with frames/objects for knowledge representation.
  • OW Prolog has been created in order to answer Prolog's lack of graphics and interface.
  • Logtalk is an object-oriented logic programming language that can use most Prolog implementations as a back-end compiler. As a multi-paradigm language, it includes support for both prototypes and classes, protocols (interfaces), component-based programming through category-based composition, event-driven programming, and high-level multi-threading programming.
  • Prolog-MPI is an open-source SWI-Prolog extension for distributed computing over the Message Passing Interface.
  • Oblog is a small, portable, Object-oriented extension to Prolog by Margaret McDougall of EdCAAD, University of Edinburgh.
  • The Gödel programming language is a strongly-typed implementation of Concurrent constraint logic programming. It is built on SICStus Prolog.

Related languages

  • Visual Prolog, also formerly known as PDC Prolog and Turbo Prolog. Visual Prolog is a strongly typed object-oriented dialect of Prolog, which is considerably different from standard Prolog. As Turbo Prolog it was marketed by Borland, but it is now developed and marketed by the Danish firm PDC (Prolog Development Center) that originally produced it.
  • Datalog is a subset of Prolog. It is limited to relationships that may be stratified and does not allow compound terms. In contrast to Prolog, Datalog is not Turing-complete.
  • CSC GraphTalk is a proprietary implementation of Warren's Abstract Machine, with additional object-oriented properties.
  • In some ways Prolog is a subset of Planner. The ideas in Planner were later further developed in the Scientific Community Metaphor.

Frameworks also exist which can provide a bridge between Prolog and the Java programming language:

  • JPL is a bi-directional Java Prolog bridge which ships with SWI-Prolog by default, allowing Java and Prolog to call each other respectively. It is known to have good concurrency support and is under active development.
  • InterProlog, a programming library bridge between Java and Prolog, implementing bi-directional predicate/method calling between both languages. Java objects can be mapped into Prolog terms and vice-versa. Allows the development of GUIs and other functionality in Java while leaving logic processing in the Prolog layer. Supports XSB, SWI-Prolog and YAP.
  • Prova provides native syntax integration with Java, agent messaging and reaction rules. Prova positions itself as a rule-based scripting (RBS) system for middleware. The language breaks new ground in combining imperative and declarative programming.

See also


  • William F. Clocksin, Christopher S. Mellish: Programming in Prolog: Using the ISO Standard. Springer, 5th ed., 2003, ISBN 978-3540006787. (This edition is updated for ISO Prolog. Previous editions described Edinburgh Prolog.)
  • William F. Clocksin: Clause and Effect. Prolog Programming for the Working Programmer. Springer, 2003, ISBN 978-3540629719.
  • Michael A. Covington, Donald Nute, Andre Vellino, Prolog Programming in Depth, 1996, ISBN 0-13-138645-X.
  • Michael A. Covington, Natural Language Processing for Prolog Programmers, 1994, ISBN 0-13-62921
  • Robert Smith, John Gibson, Aaron Sloman: 'POPLOG's two-level virtual machine support for interactive languages', in Research Directions in Cognitive Science Volume 5: Artificial Intelligence, Eds D. Sleeman and N. Bernsen, Lawrence Erlbaum Associates, pp 203-231, 1992.
  • Leon Sterling and Ehud Shapiro, The Art of Prolog: Advanced Programming Techniques, 1994, ISBN 0-262-19338-8.
  • Ivan Bratko, PROLOG Programming for Artificial Intelligence, 2000, ISBN 0-201-40375-7.
  • Robert Kowalski, The Early Years of Logic Programming, CACM January 1988.
  • ISO/IEC 13211: Information technology — Programming languages — Prolog. International Organization for Standardization, Geneva.
  • Alain Colmerauer and Philippe Roussel, The birth of Prolog, in The second ACM SIGPLAN conference on History of programming languages, p. 37-52, 1992.
  • Richard O'Keefe, The Craft of Prolog, ISBN 0-262-15039-5.
  • Patrick Blackburn, Johan Bos, Kristina Striegnitz, Learn Prolog Now!, 2006, ISBN 1-904987-17-6.
  • David H D Warren, Luis M. Pereira and Fernando Pereira, Prolog - the language and its implementation compared with Lisp. ACM SIGART Bulletin archive, Issue 64. Proceedings of the 1977 symposium on Artificial intelligence and programming languages, pp 109 - 115.

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