Prolog is a logic programming language. It is a general purpose language often associated with artificial intelligence and computational linguistics. It has a purely logical subset, called "pure Prolog", as well as a number of extralogical features.
Having its roots in formal logic, and unlike many other programming languages, Prolog is declarative: The 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.
The language was first conceived by a group around Alain Colmerauer in Marseille, France, in the early 1970s, while the first compiler was written by David H. D. Warren in Edinburgh, Scotland. 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, and 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: programmation en logique (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.
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.
H :- B1, …, Bn..
The application of the theorem-prover treats such clauses as procedures:
B1 and … and
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.
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. It is composed of a sequence of characters that is parsed by the Prolog reader as a single unit. Atoms are usually bare words in Prolog code, written with no special syntax. However, atoms containing spaces or certain other special characters must be surrounded by single quotes. Atoms beginning with a capital letter must also be quoted, to distinguish them from variables. The empty list, written
, is also an atom. Other examples of atoms include
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 variable can become instantiated (bound to equal a specific term) via unification. A single underscore (
_) denotes an anonymous variable and means "any term". Unlike other variables, the underscore does not represent the same value everywhere it occurs within a predicate definition.
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]). Compound terms with functors that are declared as operators can be written in prefix or infix notation. For example, the terms
=(X,Y) can also be written as
X=Y, respectively. Users can declare arbitrary functors as operators with different precedences to allow for domain-specific notations. The notation f/n is commonly used to denote a term with functor f and arity n.
Special cases of compound terms:
is a list. A compound term with functor
.(dot) and arity 2, whose second argument is a list, is itself a list. There exists special syntax for denoting lists:
.(A, B)is equivalent to
[A|B]. For example, the list
.(1, .(2, .(3, )))can also be written as
[1 | [2 | [3 | ]]], or even more compactly as
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 above fact, one can ask:
is tom a cat?
?- cat(tom). Yes
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
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:
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). mother_child(trude, sally). father_child(tom, sally). father_child(tom, erica). father_child(mike, tom).
This results in the following query being evaluated as true:
?- sibling(sally, erica). Yes
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.
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.
legal(X) :- \+ illegal(X).
is evaluated as follows: Prolog attempts to prove 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. 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.
Under a declarative reading, the order of rules, and of goals within rules, is irrelevant since logical disjunction and conjunction are commutative. Procedurally, however, it is often important to take into account Prolog's execution strategy, either for efficiency reasons, or due to the semantics of impure built-in predicates for which the order of evaluation matters.
There is a special notation called definite clause grammars (DCGs). 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 straight-forward 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.
A larger example will show the potential of using Prolog in parsing.
Given the sentence expressed in BNF:
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],