Expert Systems/Prolog

History
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.

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.

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.

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

Example atoms are: x, blue, 'Some atom', and [].

Numbers can be floats or integers. Many Prolog implementations also provide unbounded integers and rational numbers.

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 via unification. A single underscore (_) denotes an anonymous variable and means "any term".

A compound term has a functor and a number of arguments, which are again terms. The number of arguments is called the term's arity. (Atoms can be regarded as a special case of compound terms of arity zero.)

Examples for terms are f(a,b) and p(x,y,z). Some operators can be used in infix notation. For example, the terms +(a,b) and =(X,Y) can also be written as a+b and X=Y, respectively. Users can also declare arbitrary sequences of symbols as new infix or postfix operators 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:


 * Lists are defined inductively: The atom [] 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].
 * 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.

Programming in Prolog
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 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:

cat(tom).

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 (<tt>append(ListA, ListB, X)</tt> given lists <tt>ListA</tt> and <tt>ListB</tt>) as well as to split a given list into parts (<tt>append(X, Y, List)</tt>, given a list <tt>List</tt>). 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 <tt>write/1</tt> displays a term on the screen.

Evaluation
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 (X = Y). 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.

Negation
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 is 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.

Operational considerations
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.

DCGs and parsing
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.

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

Given the sentence expressed in BNF:

<stat_part>  ::=  | <stat_part> ::= <id> = ; ::= |        ::=  <id> | <id>         ::=  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)) etc.

Higher-order programming
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.

Implementation techniques
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.

Examples
Here follow some example programs written in Prolog.

QuickSort
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:

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]

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

Extensions

 * 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 open source object-oriented extension to the Prolog programming language. Integrating logic programming with object-oriented and event-driven programming, it is compatible with most Prolog compilers. It supports both prototypes and classes. In addition, it supports component-based programming through category-based composition.
 * Prolog-MPI is an open-source SWI-Prolog extension for distributed computing over the Message Passing Interface.

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 actually 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.
 * In some ways Prolog is a subset of Planner, e.g., see Kowalski's early history of logic programming. 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:


 * 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.