Logic programming is, in its broadest sense, the use of mathematical logic for computer programming. In this view of logic programming, which can be traced at least as far back as Alonzo Church [1932], logical inference can be used in programming. This view was further developed by John McCarthy's [1958] advicetaker proposal to use forward chaining under the control of logical propositions. The Planner programming language [1969, 1971] used both forward chaining (invoked by assertions) and backward chaining (invoked by goals).^{[1]}
However, Kowalski ^{[2]} restricts logic programming to backwards chaining in the form:



 G if G_{1} and and G_{n}
that treats the implications as goalreduction procedures:



 to show/solve G, show/solve G_{1} and and G_{n}.
For example, it treats the implication:



 The drive cab is alerted if an alarm signal button is pressed.
as a procedure that from the goal "the drive cab is alerted" generates the subgoal "an alarm signal button is pressed."
Note that this is consistent with the BHK interpretation of constructive logic, where implication would be interpreted as a solution of problem G given solutions of G_{1} G_{n}.
The defining feature of logic programming is that sets of formulas can be regarded as programs and proof search can be given a computational meaning. In some approaches the underlying logic is restricted, e.g., Horn clauses or Hereditary Harrop formulas. See D. Miller et al., 1991.
As in the purely declarative case, the programmer is responsible for ensuring the truth of programs. But since automated proof search is generally infeasible, logic programming as commonly understood also relies on the programmer to ensure that inferences are generated efficiently (see problem solving). In many cases, to achieve efficiency, one needs to be aware of and to exploit the problemsolving behavior of the theoremprover. In this respect, logic programming is comparable to conventional imperative programming; using programs to control the behavior of a program executor. However, unlike conventional imperative programs, which have only a procedural interpretation, logic programs also have a declarative, logical interpretation, which helps to ensure their correctness. Moreover, such programs, being declarative, are at a higher conceptual level than purely imperative programs; and their program executors, being theoremprovers, operate at a higher conceptual level than conventional compilers and interpreters.
History
Logic programming in the first and wider sense gave rise to a number of implementations, such as those by Fischer Black (1964), James Slagle (1965) and Cordell Green (1969), which were questionanswering systems in the spirit of McCarthy's Advice taker. Foster and Elcock's Absys (1969), on the other hand, was probably the first language to be explicitly developed as an assertional programming language.
Logic programming in the narrower sense can be traced back to debates in the late 1960s and early 1970s about declarative versus procedural representations of knowledge in Artificial Intelligence. Advocates of declarative representations were notably working at Stanford, associated with John McCarthy, Bertram Raphael and Cordell Green, and in Edinburgh, with J. Alan Robinson (an academic visitor from Syracuse University), Pat Hayes, and Robert Kowalski. Advocates of procedural representations were mainly centered at MIT, under the leadership of Marvin Minsky and Seymour Papert.
Although it was based on logic, Planner, developed at MIT, was the first language to emerge within this proceduralist paradigm [Hewitt, 1969]. Planner featured patterndirected invocation of procedural plans from goals (i.e. forward chaining) and from assertions (i.e. backward chaining). The most influential implementation of Planner was the subset of Planner, called MicroPlanner, implemented by Gerry Sussman, Eugene Charniak and Terry Winograd. It was used to implement Winograd's naturallanguage understanding program SHRDLU, which was a landmark at that time. To cope with the very limited memory systems at the time, Planner used a backtracking control structure so that only one possible computation path had to be stored at a time. Planner gave rise to the programming languages QA4, Popler, Conniver, QLISP, and the concurrent language Ether.
Hayes and Kowalski in Edinburgh tried to reconcile the logicbased declarative approach to knowledge representation with Planner's procedural approach. Hayes (1973) developed an equational language, Golux, in which different procedures could be obtained by altering the behavior of the theorem prover. Kowalski, on the other hand, showed how SLresolution treats implications as goalreduction procedures. Kowalski collaborated with Colmerauer in Marseille, who developed these ideas in the design and implementation of the programming language Prolog. Prolog gave rise to the programming languages ALF, Fril, G del, Mercury, Oz, Ciao, Visual Prolog, XSB, and Prolog, as well as a variety of concurrent logic programming languages (see Shapiro (1989) for a survey), constraint logic programming languages and datalog.
In 1997, the Association of Logic Programming bestowed to fifteen recognized researchers in logic programming the title Founders of Logic Programming to recognize them as pioneers in the field:

Maurice Bruynooghe (Belgium)

Jacques Cohen (US)

Alain Colmerauer (France)

Keith Clark (UK)

Veronica Dahl (Canada/Argentina)

Maarten van Emden (Canada)

Herve Gallaire (France)

Robert Kowalski (UK)

Jack Minker (US)

Fernando Pereira (US)

Luis Moniz Pereira (Portugal)

Ray Reiter (Canada)

J. Alan Robinson (US)

Peter Szeredi (Hungary)

David H. D. Warren (UK)
Prolog
The programming language Prolog was developed in 1972 by Alain Colmerauer. It emerged from a collaboration between Colmerauer in Marseille and Robert Kowalski in Edinburgh. Colmerauer was working on natural language understanding, using logic to represent semantics and using resolution for questionanswering. During the summer of 1971, Colmerauer and Kowalski discovered that the clausal form of logic could be used to represent formal grammars and that resolution theorem provers could be used for parsing. They observed that some theorem provers, like hyperresolution, behave as bottomup parsers and others, like SLresolution (1971), behave as topdown parsers.
It was in the following summer of 1972, that Kowalski, again working with Colmerauer, developed the procedural interpretation of implications. This dual declarative/procedural interpretation later became formalised in the Prolog notation
 H : B_{1}, , B_{n}.
which can be read (and used) both declaratively and procedurally. It also became clear that such clauses could be restricted to definite clauses or Horn clauses, where H, B_{1}, , B_{n} are all atomic predicate logic formulae, and that SLresolution could be restricted (and generalised) to LUSH or SLDresolution. Kowalski's procedural interpretation and LUSH were described in a 1973 memo, published in 1974.
Colmerauer, with Philippe Roussel, used this dual interpretation of clauses as the basis of Prolog, which was implemented in the summer and autumn of 1972. The first Prolog program, also written in 1972 and implemented in Marseille, was a French questionanswering system. The use of Prolog as a practical programming language was given great momentum by the development of a compiler by David Warren in Edinburgh in 1977. Experiments demonstrated that Edinburgh Prolog could compete with the processing speed of other symbolic programming languages such as Lisp. Edinburgh Prolog became the de facto standard and strongly influenced the definition of ISO standard Prolog.
Negation as failure
MicroPlanner had a construct, called "thnot", which when applied to an expression returns the value true if (and only if) the evaluation of the expression fails. An equivalent operator is normally builtin in modern Prolog's implementations and has been called "negation as failure". It is normally written as not(p), where p is an atom whose variables normally have been instantiated by the time not(p) is invoked. A more cryptic (but standard) syntax is \+ p . Negation as failure literals can occur as conditions not(B_{i}) in the body of program clauses.
The logical status of negation as failure was unresolved until Keith Clark [1978] showed that, under certain natural conditions, it is a correct (and sometimes complete) implementation of classical negation with respect to the completion of the program. Completion amounts roughly to regarding the set of all the program clauses with the same predicate on the left hand side, say
 H : Body_{1}.
 H : Body_{k}.
as a definition of the predicate
 H iff (Body_{1} or or Body_{k})
where "iff" means "if and only if". Writing the completion also requires explicit use of the equality predicate and the inclusion of a set of appropriate axioms for equality. However, the implementation of negation by failure needs only the ifhalves of the definitions without the axioms of equality.
The notion of completion is closely related to McCarthy's circumscription semantics for default reasoning, and to the closed world assumption.
As an alternative to the completion semantics, negation as failure can also be interpreted epistemically, as in the stable model semantics of answer set programming. In this interpretation not(B_{i}) means literally that B_{i} is not known or not believed. The epistemic interpretation has the advantage that it can be combined very simply with classical negation, as in "extended logic programming", to formalise such phrases as "the contrary can not be shown", where "contrary" is classical negation and "can not be shown" is the epistemic interpretation of negation as failure.
Problem solving
In the simplified, propositional case in which a logic program and a toplevel atomic goal contain no variables, backward reasoning determines an andor tree, which constitutes the search space for solving the goal. The toplevel goal is the root of the tree. Given any node in the tree and any clause whose head matches the node, there exists a set of child nodes corresponding to the subgoals in the body of the clause. These child nodes are grouped together by an "and". The alternative sets of children corresponding to alternative ways of solving the node are grouped together by an "or".
Any search strategy can be used to search this space. Prolog uses a sequential, lastinfirstout, backtracking strategy, in which only one alternative and one subgoal is considered at a time. Other search strategies, such as parallel search, intelligent backtracking, or bestfirst search to find an optimal solution, are also possible.
In the more general case, where subgoals share variables, other strategies can be used, such as choosing the subgoal that is most highly instantiated or that is sufficiently instantiated so that only one procedure applies. Such strategies are used, for example, in concurrent logic programming.
The fact that there are alternative ways of executing a logic program has been characterised by the equation:
Algorithm = Logic + Control
where "Logic" represents a logic program and "Control" represents different theoremproving strategies.^{[3]}
Knowledge representation
The fact that Horn clauses can be given a procedural interpretation and, vice versa, that goalreduction procedures can be understood as Horn clauses + backward reasoning means that logic programs combine declarative and procedural representations of knowledge. The inclusion of negation as failure means that logic programming is a kind of nonmonotonic logic.
Despite its simplicity compared with classical logic, this combination of Horn clauses and negation as failure has proved to be surprisingly expressive. For example, it has been shown to correspond, with some further extensions, quite naturally to the semiformal language of legislation. It is also a natural language for expressing commonsense laws of cause and effect, as in the situation calculus and event calculus.
Abductive logic programming
Abductive Logic Programming is an extension of normal Logic Programming that allows some predicates, declared as abducible predicates, to be incompletely defined. Problem solving is achieved by deriving hypotheses expressed in terms of the abducible predicates as solutions of problems to be solved. These problems can be either observations that need to be explained (as in classical abductive reasoning) or goals to be achieved (as in normal logic programming). It has been used to solve problems in Diagnosis, Planning, Natural Language and Machine Learning. It has also been used to interpret Negation as Failure as a form of abductive reasoning.
Metalogic programming
Because mathematical logic has a long tradition of distinguishing between object language and metalanguage, logic programming also allows metalevel programming. The simplest metalogic program is the socalled "vanilla" metainterpreter:
solve(true). solve((A,B)): solve(A),solve(B). solve(A): clause(A,B),solve(B).
where true represents an empty conjunction, and clause(A,B) means there is an objectlevel clause of the form A : B.
Metalogic programming allows objectlevel and metalevel representations to be combined, as in natural language. It can also be used to implement any logic that is specified by means of inference rules.
Constraint logic programming
Constraint logic programming is an extension of normal Logic Programming that allows some predicates, declared as constraint predicates, to occur as literals in the body of clauses. These literals are not solved by goalreduction using program clauses, but are added to a store of constraints, which is required to be consistent with some builtin semantics of the constraint predicates.
Problem solving is achieved by reducing the initial problem to a satisfiable set of constraints. Constraint logic programming has been used to solve problems in such fields as civil engineering, mechanical engineering, digital circuit verification, automated timetabling, air traffic control, and finance. It is closely related to abductive logic programming.
Concurrent logic programming
Keith Clark, Steve Gregory, Vijay Saraswat, Udi Shapiro, Kazunori Ueda, etc. developed a family of Prologlike concurrent message passing systems using unification of shared variables and data structure streams for messages. Efforts were made to base these systems on mathematical logic, and they were used as the basis of the Japanese Fifth Generation Project (ICOT). However, the Prologlike concurrent systems were based on message passing and consequently were subject to the same indeterminacy as other concurrent messagepassing systems, such as Actors (see Indeterminacy in concurrent computation). Consequently, the ICOT languages were not based on logic in the sense that computational steps could not be logically deduced [Hewitt and Agha, 1988].
Concurrent constraint logic programming combines concurrent logic programming and constraint logic programming, using constraints to control concurrency. A clause can contain a guard, which is a set of constraints that may block the applicability of the clause. When the guards of several clauses are satisfied, concurrent constraint logic programming makes a committed choice to the use of only one.
Inductive logic programming
Inductive logic programming is concerned with generalizing positive and negative examples in the context of background knowledge. Generalizations, as well as the examples and background knowledge, are expressed in logic programming syntax. Recent work in this area, combining logic programming, learning and probability, has given rise to the new field of statistical relational learning and probabilistic inductive logic programming.
Higherorder logic programming
Several researchers have extended logic programming with higherorder programming features derived from higherorder logic, such as predicate variables. Such languages include the Prolog extensions HiLog and Prolog.
Linear logic programming
Basing logic programming within linear logic has resulted in the design of logic programming languages that are considerably more expressive than those based on classical logic. Horn clause programs can only represent state change by the change in arguments to predicates. In linear logic programming, one can use the ambient linear logic to support state change. Some early designs of logic programming languages based on linear logic include LO [Andreoli & Pareschi, 1991], Lolli [Hodas & Miller, 1994], ACL [Kobayashi & Yonezawa, 1994], and Forum [Miller, 1996]. Forum provides a goaldirected interpretation of all of linear logic.
See also
References
General introductions
Other sources
 Fisher Black. A deductive question answering system Harvard University. Thesis. 1964.
 J.M. Foster and E.W. Elcock. ABSYS 1: An Incremental Compiler for Assertions: an Introduction,, Machine Intelligence 4, Edinburgh U Press, 1969, pp. 423 429
 Cordell Green. Application of Theorem Proving to Problem Solving IJCAI 1969.
 Pat Hayes. Computation and Deduction. In Proceedings of the 2nd MFCS Symposium. Czechoslovak Academy of Sciences, 1973, pp. 105 118.
 Carl Hewitt. Planner: A Language for Proving Theorems in Robots IJCAI 1969.
 Joshua Hodas and Dale Miller. Logic Programming in a Fragment of Intuitionistic Linear Logic, Information and Computation, 1994, 110(2), 327365.
 Naoki Kobayashi and Akinori Yonezawa. Asynchronous communication model based on linear logic, Formal Aspects of Computing, 1994, 279294.
 Robert Kowalski and Donald and Kuehner, '''Linear Resolution with Selection Function''' Artificial Intelligence, Vol. 2, 1971, pp. 227 60.
 Robert Kowalski '''Predicate Logic as a Programming Language''' Memo 70, Department of Artificial Intelligence, Edinburgh University. 1973. Also in Proceedings IFIP Congress, Stockholm, North Holland Publishing Co., 1974, pp. 569 574.
 John McCarthy. Programs with common sense Symposium on Mechanization of Thought Processes. National Physical Laboratory. Teddington, England. 1958.
 D. Miller, G. Nadathur, F. Pfenning, A. Scedrov. Uniform proofs as a foundation for logic programming, Annals of Pure and Applied Logic, vol. 51, pp 125 157, 1991.
 Ehud Shapiro (Editor). Concurrent Prolog MIT Press. 1987.
 Ehud Shapiro. The family of concurrent logic programming languages ACM Computing Surveys. September 1989.
 James Slagle. Experiments with a Deductive QuestionAnswering Program CACM. December 1965.
 Shunichi Uchida and Kazuhiro Fuchi Proceedings of the FGCS Project Evaluation Workshop Institute for New Generation Computer Technology (ICOT). 1992.
Further reading
 Carl Hewitt. Procedural Embedding of Knowledge In Planner IJCAI 1971.
 Carl Hewitt. The repeated demise of logic programming and why it will be reincarnated What Went Wrong and Why: Lessons from AI Research and Applications. Technical Report SS0608. AAAI Press. March 2006.
 Evgeny Dantsin, Thomas , Georg Gottlob, Andrei Voronkov: Complexity and expressive power of logic programming. ACM Comput. Surv. 33(3): 374425 (2001)
 Ulf Nilsson and Jan Maluszynski, Logic, Programming and Prolog
External links
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