Logic programming language 

PROgrammation en LOGique. Alain Colmerauer and Phillipe Roussel, U Aix-Marseille 1971.

Designed originally for natural-language processing. LUSH (or SLD) resolution theorem proving based on the unification algorithm. No user-defined functions, and no control structure other than the built-in depth-first search with backtracking. Consists of a rich collection of data structures, and a powerful notation for encoding end-user applications.It has logical and declarative aspects, interpretive nature, compactness, and inherent modularity. It solves problems by searching a knowledge base (or more correctly database) which would be greatly improved if several processors are made to search different parts of the database.  

Early collaboration between Marseille and R. Kowalski at U Edinburgh continued until about 1975.

First implemented 1972 in ALGOL-W.

ref: Hutchins 2001
" At Montreal, research began in 1970 on a syntactic transfer system for English-French translation. The TAUM project (Traduction Automatique de l"Université de Montréal) had two major achievements: firstly, the Q-system formalism for manipulating linguistic strings and trees (later developed as the Prolog programming language), and secondly, the Météo system for translating weather forecasts."

Related languages
ALGOL W => Prolog   Written using
microPLANNER => Prolog   Influence
Q-system => Prolog   Evolution of
Robinson Unification Logic => Prolog   Incorporated some features of
Prolog => Action Languages   Extension of
Prolog => AdLog   Implementation
Prolog => AMLOG   Extension of
Prolog => ANDOR   Extension of
Prolog => Aquarius Prolog   Implementation
Prolog => ASTLOG   Augmentation of
Prolog => BeBOP   Extension of
Prolog => Beta-Prolog   Extension of
Prolog => BNR Prolog   Evolution of
Prolog => Cactus   Extension of
Prolog => CLP   Extension of
Prolog => CLP(R)   Superset
Prolog => Complex-Prolog   Extension of
Prolog => Concurrent Prolog   Extension of
Prolog => CORAL   Derivation of
Prolog => CUP   Evolution of
Prolog => cu-Prolog   Extension of
Prolog => DATALOG   Evolution of
Prolog => DLP   Based on
Prolog => d-Prolog   Extension of
Prolog => ECRC-Prolog   Extension of
Prolog => Edinburgh Prolog   Dialect of
Prolog => EPILOG   Extension to
Prolog => EPOCH   Extension of
Prolog => EqL   Derivation of
Prolog => ESP   Extension of
Prolog => GHC   Evolution of
Prolog => GNOSIS   Incorporated features of
Prolog => Golog   Extension of
Prolog => GraphLog   Influence
Prolog => HCPRVR   Implementation
Prolog => IB-Templog   Extension of
Prolog => IC-Prolog   Augmentation of
Prolog => InTense   Extension of
Prolog => Intermission   Extension of
Prolog => ISO Prolog   Standardisation
Prolog => Kanji PROLOG   Augmentation of
Prolog => KL0   Evolution of
Prolog => KNOWLOG   Extension of
Prolog => lambda-Prolog   Evolution of
Prolog => LaSt   Extension of
Prolog => LESK   Extension of
Prolog => LM-Prolog   Implementation
Prolog => LOGAL   Extension of
Prolog => Logicon   Subsumed
Prolog => LOGIN   Extension of
Prolog => LOGLISP   Influence
Prolog => LOGLisp   Extension of
Prolog => LogTalk   Extension of
Prolog => lolli   Extension of
Prolog => LPL   Extension to
Prolog => Maglog   Extension of
Prolog => Marseille Prolog   Dialect of
Prolog => MCPL   Influence
Prolog => METALOG   Extension of
Prolog => Meta-Logic Prolog   Extension of
Prolog => Modal Logic Programming   Extension of
Prolog => Modal Prolog   Extension of
Prolog => MODEL   Influence
Prolog => Modula-Prolog   Incorporated some features of
Prolog => MODULOG   Incorporated some features of
Prolog => Molog   Extension of
Prolog => MTL   Extension of
Prolog => MU-Prolog   Extension of
Prolog => N-Prolog   Evolution of
Prolog => ObjVProlog   Evolution of
Prolog => OL(P)   Evolution of
Prolog => ParaLog   Extension of
Prolog => Parlog   Evolution of
Prolog => Paslog   Incorporated some features of
Prolog => Path Pascal   Incorporated some features of
Prolog => Pathlog   Extension of
Prolog => POEM   Evolution of
Prolog => POPLOG   Implementation
Prolog => ProBoole   Extension of
Prolog => ProFIT   Extension of
Prolog => Progol   Extension of
Prolog => PROLIX   Extension of
Prolog => Prolog/KR   Extension of
Prolog => Prolog++   Extension of
Prolog => Prolog-D-Linda   Evolution of
Prolog => Prolog-ELF   Extension of
Prolog => Prolog-Linda   Augmentation of
Prolog => Prolog-Linda   Augmentation of
Prolog => QLOG   Incorporates some features of
Prolog => Quintec Prolog   Implementation of
Prolog => R-Prolog   Extension of
Prolog => Sample   Extension of
Prolog => SB-Prolog   Implementation
Prolog => SCOOP   Extension of
Prolog => SEL   Evolution of
Prolog => SetLog   Derivation of
Prolog => Shared Prolog   Adaptation of
Prolog => SICStus Prolog   Implementation
Prolog => SOLO   Influence
Prolog => Spool   Extension of
Prolog => Starlog   Extension of
Prolog => Temporal Prolog   Extension of
Prolog => THLP   Augmentation of
Prolog => Tokio   Based on
Prolog => Trilogy   Incorporated some features of
Prolog => Turbo Prolog   Evolution of
Prolog => TyRuBa   Extension of
Prolog => UNIFORM   Incorporated features of
Prolog => Uranus   Extension of
Prolog => Van Emden and Kowalsi extended Prolog   Generalisation of
Prolog => Vienna Parallel Logic   Influence
Prolog => Waterloo Core Prolog   Implementation
Prolog => W-Prolog   Implementation
Prolog => XSB   Extension of

  • Andreka, H and Nemeti, I "The generalised completeness of Horn predicate-logic as a programming language" University of Edinburgh. Department of artificial intelligence. Research report 21 1976 view details
  • Szeredi, P., "Prolog a Very High Level Language Based on Predicate Logic" view details
          in Second Hungarian Conference on Computer Science (June, 1977) view details
  • Warren, David H.D., Luis M. Pereira, and Fernando C.N. Pereira, "Prolog: The Language and its Implementation Compared with LISP" view details
          in SIGPLAN Notices 12(08) August 1977 "Symposium on Artificial Intelligence and Programming Languages" view details
  • Colmerauer, A. and Roussel, P. "The birth of Prolog" view details
          in SIGPLAN Notices 14(04) April 1979 including The first ACM SIGPLAN conference on History of programming languages (HOPL) Los Angeles, CA, June 1-3, 1978 view details
  • Kahn, Kenneth M. "Uniform : a language based upon unification which unifies (much of) Lisp, Prolog, and Act 1" UPMAIL. Uppsala programming methodology and artificial intelligence laboratory. Technical reports 17 Uppsala University 1981 view details Abstract: Uniform is an AI programming language under development based upon augmented unification. It is an attempt to combine, in a simpie coherent framework, the most important features of Lisp, actor languages such as Act 1 and SmallTalk, and logic programming languages such as Prolog. Among the unusual abilities of the language is its ability to use the same program as a function, an inverse function, a predicate, a pattern, or a generator. All of these uses can be performed upon concrete, symbolic, and partially instantiated data. Uniform features automatic inheritance from multiple super classes, facilities for manipulation of programs, a limited ability to determine program equivalence, and a unification-oriented database.
    Extract: A Language based upon Unification
    A Language based upon Unification
    Uniform is based upon the idea of an extensible unification procedure. All programs are extensions to the unification process. Unification piavs the roles of pattern matching, evaluation, message passing, inheritance, and symbolic evaluation. In the process of unifying the factorial of 3 with an integer n, n is unified with 6. The concatenation of x and the list (c d) unifies with the list (a b c d), resulting in x being unified with the list (a b). Unifying the nth element of the list of all prime numbers with 5 results in n unified with 3. Unifying the reverse of a list of variables x and y with a list z, unifies z with a list of v and x. Unifying a member of set x with 3 member of set y yields a member of the intersection of x and y. Unifying a description of red chairs with a description of big chairs produces a description of big red chairs.    And so on.
    Extract: Unification — What it is
    Unification - What it is
    Unification was invented for use in resolution theorem provers [Robinson 1965] and has since been used in a few programming languages. Two well-known examples are Prolog which is a programming language based upon resolution theorem proving [Warren 1977] and Qlisp [Sacerdoti 1976]. In these languages unification is only one facility among others. In Uniform unification is augmented so that no other mechanism such as resolution, automatic-backtracking, or evaluation is needed.
    Unification is the process of generating the most general common instance of a set of descriptions. It is implemented as a process that returns a "unifier" which if applied to any of the original descriptions produces the sought after instance. In
    the most common case, the unifier is simply an environment describing variable
    bindings and it is applied to a description by substituting its values for the
    variables in the description. It is the use of unifiers, rather than actualiv
    constructing instances, which makes the process computationally feasible in much
    the same wav that environments replace lambda substitution in Lisp. As an example,
    (unifier-of(foo x 'a r) (foo 'b v r))
    produces "(unifier (x 'b) (y 'a))" which if substituted into either description produces the instance "(foo 'b 'a r)". Unification is defined to produce the most general instance which means that the instance produced must unify with all other possible instances.
    Pattern matching, which has played such a large role in most AI languages, is a special case of the unification of two descriptions. The pattern is a description containing variables which is matched against a description without any variables. If the match succeeds, the unifier produced is a set of match bindings which when substituted into the pattern produces the other description.
    Unification has three advantages over pattern matching.
    1. There is no distinction between patterns which bind variables and patterns which use the value of variables. This allows the meaning of a pattern to be determined dynamically and is crucial in using programs in multiple ways and directions.
    2. Patterns can be matched against patterns. This is important for dealing with partial information. For example, in a unification-oriented database one can have a demon which triggers whenever a cube is put on a ball and then add the "pattern" (on (ball 'a) (cube x)) and the demon will be triggered even though x has no value (it represents an undetermined cube upon ball a). It is also useful in determining if one pattern is a special case of another. Uniform uses unification this way so that by default more specific knowledge is used first.
    3. The order in which sub-problems (recursive calls to unify) are made does not affect the outcome. This gives the implementation a freedom of optimization not possible in most pattern matchers. Parallel implementations of unification which could take advantage of the parallel hardware of the future are also possible.

    Extract: How Unification is Augmented in Uniform
    How Unification is Augmented in Uniform
    Unification is a syntactic process. Its only concern is that forms have the same "head", their arguments unify, constants are equal and recursively that bindings unify. It cannot unify the sum of x and 3 with 7 by unifying x with *. Uniform's augmented unification leaves it up to the forms involved to unify as they see fit where the traditional syntactic unification process is used only as a default. Except for a small set of primitive forms this augmentation is described in Uniform by the user.
    The only constraint upon augmentations to the unification of two descriptions is that the resulting unifier produce equivalent descriptions when applied. For example, the unifier describing x as » produces 7 and the sum of A few of the primitive forms stretch this view of unification. For example, the primitive form "(print)" will unify with anything and as a side effect the other form will be printed. The primitive "(ground)11 will either unifv with any constant or will eventually fail if the other is a variable or contains variables that are never bound. There are primitives for dynamically creating unification variables and forms, primitives for escaping to Lisp's Eval (a theoretically unnecessary, yet very useful, primitive), for forcing sequentiaiity (critical in situations involving input-output or other side effects), for determining if two things are the exact same object {Lisp's EQ, critical when dealing with circular structures and important for efficiency), and for the logical connectors "and", "or11, and "not". Without this small set of primitives, unification would not be adequate as the sole basis of a programming language.
    Uniform's augmented unification not only has the unusual features of delegating to the forms involved but is based upon a new very parallel algorithm.
    Sub-unifications of corresponding arguments are computed independents and the resulting unifiers are unified. A unifier is a virtual set of possible bindings where further possibilities are computed only upon need. This mechanism replaces the automatic backtracking in Prolog and Qlisp. In addition, when the descriptions cannot be unified the algorithm produces failure descriptions which are used internally and by the user for debugging. Unification is also used to implement the occur check and the "ground" primitive. The algorithm is described further in [Kahn forthcoming] Extract: Uniform and Actor Languages
    Uniform and Actor Languages
    Computer languages based upon computational entities called "actors" offer modularity, parallelism, full extensibility of both data and functions and a simpie but powerful computational semantics. An early version of Uniform was attempted in Act 1 , a language that takes the idea of actors to the extreme. Many of the facilities of Act I would have been available in Uniform, including its excellent primitives for describing concurrent computation. Unfortunately the current implementation of Act 1 is too slow to build a practical interpreter upon it.
    Act I is a message passing language based upon the convention that actors be able to respond to "eval" and "match" messages. Uniform can be viewed as a language in which forms pass "unify" messages between themselves and their parts. As we saw in the previous section, unification subsumes evaluation. Unification clearly subsumes the match messages in Act 1 since pattern matching is just the special case of unification where one of the forms contains no variables.
    One of the important features of actor languages is the ability to describe a new data structure and have old programs use it without modification. This is a consequence of the fact that programs depend only upon the behavior of data in response to messages. A list is any actor which can answer "first" and "rest" messages.     The  analogous  statement  about  Uniform  is  that  a  list   is  any   form   that can unify with "(cons x y)". For example, suppose we want to define a new kind of list which internally is represented by two lists, one for the original members and one for those deleted. The advantage of these "delete lists" is that deletion becomes a very cheap and pure operation in return for a little overhead on other operations.
    They   can be defined as follows in Uniform:
    (= {delete-list deleted (cons first rest)) (rules first
    ((member deleted) ;is already deleted so skip it (delete-list deleted rest)) ((?) ;otherwise the first element is ok (cons first (delete-list deleted rest))))))
    This is all that is needed to run any program that works on lists since it provides a path from "delete-list" to "cons". For example, List's "delete" operation defined below will work on these lists.
    (as sert
    (= (delete x (cons first rest)) (rules x
    (first (delete x rest))
    ((?) (cons first (delete x rest))))))
    (assert (= (delete x ()) ()))
    The whole point of having "delete lists", however, is to delete elements efficiently. This can be done as  follows:
    (assert (= (delete x (delete-list deleted memb e r s ) ) (delete-list (cons x deleted) member s)) )
    Notice that this way of implementing lists as anything that can unify with a cons of two variables subsumes the inheritance mechanism in languages like SmallTalk and Director. If nothing is known about how to do some operation upon a particular kind of list then the same operation upon lists in general will be used. Uniform always tries first the shortest path between two structures. The path to delete list's delete operation is shorter than one through cons's delete so It is followed first. Of course sub-ciasses are possible. If x-lists oniy unify directly with y-lists which unify with "cons", then definitions of operations upon x-Iists will be used before those for y-lists which in turn will be used before cons's definitions.
    This same mechanism works for multiple super classes. If we define how horizontal-dashed- lines     unify     with    horizontal-lines     and    with     dashed-lines     then
    operations upon either one can be applied to horizontal-dashed-lines. Since Uniform follows shortest paths first, the multiple super classes are searched in a breadth-first fashion.
    One very important part of some of the actor languages is the user definable control structures and ability to compute in parallel [Hewitt 1977] [Lieberman draft]. This is a serious deficiency of Uniform. The plan is to add such information as advice to the interpreter as to how to go about doing the unifications. This approach is similar to one taken in Metalog [Dincbas 1980]. The appeal of separating logic from control is that a user can develop and test the logic or competence of a program before adding control information to improve its performance [Kowalski 1979] [Pratt 1977]. Also different uses of the same program may be helped by  different control information. Extract: Uniform and Logic Programming
    Uniform and Logic Programming
    In recent years a number of logic programming languages have appeared. Most notable is Prolog, a programming language which resembles Planner [Warren 1977]. Programs in Prolog are axioms in the first-order predicate logic restricted to Horn clauses. Programs are executed by a resolution theorem prover. What is special about Prolog is that it is intended as a general purpose programming language meant to compete with compiled Lisp as well as with Planner-like languages. The objection to logic as being an excessively constrained manner of reasoning is irrelevant to its worth as a pronramming language. One would not want to buiid AI programs upon an "informal" Lisp. The objection to logic that it is not concerned with control over the use of knowledge is a serious one. There are manv advantages however, to having a programming language based upon logic with a separate control component for improving performance such as IC-Prolog [Clark 19SO] or Metalog [Dincbas  1980].
    When compared with Lisp, Prolog has many advantages and a few very serious disadvantages. Prolog shares with Uniform the ability to use the same program in many  ways.   For example, the Prolog definition of "append" can be used not only  to compute the result of appending two lists together but can also be used as a predicate to verify that the result of appending two lists is a third list, as a generator of pairs of lists that append to a particular list, as a wav of finding the difference between two lists, and as a generator of triples of lists such that the first two append to form the third.
    Prolog has a few other features which Lisp lacks. Among them are the ability to compute with partially instantiated structures, a convenient way to handle multiple outputs, and the use of pattern matching instead of explicit list construction and selection. On the negative side, Prolog implementations are the result of a much smaller implementation effort than the major Lisp dialects and correspondingly lack good programming environments, i/o facilities, adequate arithmetic, and the like. Attempts to embed Prolog in Lisp (e.g. ) may alleviate this. Among Prolog's more fundamental problems are a dependence upon automatic backtracking, a lack of user control over search, and a lack of an efficient substitute for impure operations.
    Uniform was developed with the goal of capturing and improving these positive aspects of Prolog. Uniform supports all the uses of a definition that Prolog does and an additional few. For example. Uniform's definition of "append" is equivalent to Prolog's and can also be used as an implementation of segment patterns. In addition, it is all the knowledge about "append" the system needs to answer questions about program equivalence.
    For example, work is under way so that Uniform can successfully unify the following for all lists x and y.
    (= (append (reverse x) y)
    (reverse (append (reverse y) x)))
    In Uniform one can augment the unification of relations other than the "=" relation and so can write in Prolog's relational, as opposed to a functional, style. The following is a Uniform program for defining the "grandparent" relation (which can be used as the "grandchild" relation too).
    (assert (grandparent-of grandchild grandparent)
    ;the above is true if the following holds (parent-of a-parent grandparent) (parent-of grandchild a-parent))
    The program says that two variables are in the grandparent relation if a child of the first variable unifies with a parent of the second. As in Prolog the variables "grandparent" and "grandchild" are universally qualified and "a-parent" is existential (by virtue of not being in the "head"). It is equivalent to the Prolog program:
    grand parent_of(Grandchild,Grandparent) :-parent_of(A_parent,Grand parent), parent_of(Grandchild,A_parent).
    Despite the similarity, the two programs are executed differently. Uniform executes programs using augmented unification. Inside the unification it computes with virtual sets of all possible unifiers and performs searches in a shortest-first manner. Shortest-first search combined with the use of virtual sets of unifiers replaces Prolog's resolution theorem prover which depends upon svntactic unification and automatic backtracking. More details about Uniform's implementation can be found in [Kahn forthcoming] Extract: Conclusions and Future Research
    Conclusions and Future Research
    A surprising result of this research is that unification, a process of generating the most general instance of a set of descriptions, can be such a powerful basis for a programming language. Unification unifies the essence of Lisp, Act 1, and Prolog into a simple coherent framework.
    Uniform is far from complete/ Some of the major avenues of future research follow.
    1.  Developing and incorporating the dual of unification, generalization, into the language. The duality between unification and generalization is striking and the ability to implement them both using the same mechanism is surprising.
    2.  A   shortcoming  of   Uniform  and  Prolog  is   their  inability   to  use  negative information.      In   the   previous   example   of   association   lists   we   cannot prevent   a  key   from  having more than one association.     Uniform  will  be extended to use the following:
    (not (= (association-of key
    (cons (list key value) rest)) (not value))))
    This would cause the unification to fail if the key is found but the values do not unify. Negative information can be used in a default strategy which concurrently tries to unify two descriptions and to show that they  are not unifiable.
    3.  In the process of unification a variable may  acquire multiple constraints. As   a   default,   Uniform   simply   conjoins   them.      If   later   an   attempt   is made to give the variable a value, then the constraints disappear if the value  satisfies   them,   otherwise  it fails.     Inconsistent constraints  do  not cause failure unless there is an attempt to use them.    If the constraints have  a  unique   solution   then  only   that  value  can  unify   with  them,  but the system does not compute that value.    The unification of constraints appears to  be a natural place to use some of the constraint satisfaction techniques found in systems such as Thinglab [Borning 1979], Steele and Sussman's constraint system [Steele  1980] and the XPRT system [Steels 1979].     These   systems   provide  evidence   for   the   general   usefulness   of describing much of computation in terms of constraints.
    4. We have explored the unification of descriptions of programs. Exploring the unification of other complex structures such as frames, scripts, and units should be equally valuable. Much of what systems such as FRL, XPRT, SAM and KRL do is match complex declarative structures with others. Unification, a more general and powerful process than pattern matching,  promises to be very useful for dealing with these structures.
    5.  As   has   been   pointed   out   elsewhere   [McCarthy    1968]    [Hayes    1973] [Pratt  1977]  [Kowalski  1979]  there is much  to be gained by  separating the  control  and  logic  components  of  an   algorithm.      Uniform   programs have much less control information in them than equivalent Lisp or Act 1  programs.    A  general search strategy is used as a default so that the factual   or   competence   component   of   programs   can   be   developed   and tested without the added complexity  of being concerned  with  efficiency. The efficiency   can be  put in later and kept lexically   separate from the rest of  the  program.    A   compiler   is   planned  which  will   be  written   in Uniform and produce Lisp code.
    6.  It   is  very   difficult  to  evaluate  the  worth  of computer  language  based solely  upon small  programs.    Prolog,  for  example,  becomes  less  desirable as   the   size   of   the   programs   grow   due   to   its   impoverished   notion   of errors   and   debugging   and   its   reliance   upon   automatic   backtracking. Work has been done to alleviate these  particular failings in Uniform,  but experience  in   using  Uniform  is  lacking.     Concurrent  with   the  research suggested  above,   Uniform  needs   to   be  tested   bv   writing   large   complex programs in it.    In this respect Uniform is way  behind Lisp et. al.

          in SIGPLAN Notices 14(04) April 1979 including The first ACM SIGPLAN conference on History of programming languages (HOPL) Los Angeles, CA, June 1-3, 1978 view details
  • Clark, K. L. and F. G. McCabe "PROLOG: a language for implementing expert systems" pp455-470 view details
          in Machine Intelligence 10, J.E. Hayes, Donald Michie, and Y-H. Pao, editors, Ellis Horwood Ltd., Chicester, England, 1982. view details
  • Szeredi, P., "Module Concept for Prolog," Proceedings of the Prolog Programming Environments Workshop (1982) view details
          in Machine Intelligence 10, J.E. Hayes, Donald Michie, and Y-H. Pao, editors, Ellis Horwood Ltd., Chicester, England, 1982. view details
  • Lima, I.G. "Programming Decentralised Computers" Department of Computing Science, University of Newcastle upon Tyne, 1984 view details External link: Online copy
          in Machine Intelligence 10, J.E. Hayes, Donald Michie, and Y-H. Pao, editors, Ellis Horwood Ltd., Chicester, England, 1982. view details
  • Clocksin, W.F. & C.S. Mellish, "Programming in Prolog", Springer 1985. view details
          in Machine Intelligence 10, J.E. Hayes, Donald Michie, and Y-H. Pao, editors, Ellis Horwood Ltd., Chicester, England, 1982. view details
  • Colmerauer, Alain "Prolog in 10 figures" view details
          in [ACM] CACM 28(12) (Dec 1985) view details
  • Subrahmanyam, P.A. "The 'Software Engineering' of Expert Systems: Is Prolog Appropriate?" view details Abstract: This paper is a preliminary assessment of the viability of Prolog as a basis for the design of expert systems, where the major competition is assumed to be from Lisp and Lisp-based systems. We critically examine the basic features of Prolog from various perspectives to see to what extent they support (or hinder) expert system development. Our conclusion is that while Prolog has significant assets along several dimensions, Prolog as it exists today needs to be modified and appropriately enhanced to make it competitive to extant Lisp-based systems; we suggest the nature of some of these modifications.
          in [ACM] CACM 28(12) (Dec 1985) view details
  • Sterling, Leon and Ehud Shapiro "The Art of Prolog" view details External link: Exercise answers in Strawberry prolog External link: Code examples
          in [ACM] CACM 28(12) (Dec 1985) view details
  • Casanova, M. A. and A. L. Furtado "String pattern-matching in Prolog" view details
          in Computer Languages 13(3-4) view details
  • Cohen, Jacques "A view of the origins and development of Prolog" view details
          in [ACM] CACM 31(01) (Jan 1988). view details
  • Norvig, Peter "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp" Morgan Kaufmann 1991 view details ftpSource code
          in [ACM] CACM 31(01) (Jan 1988). view details
  • Hutchins, W.John "Machine translation over fifty years" view details Extract: Hutchins, W.John "Machine translation over fifty years"
    At Montreal, research began in 1970 on a syntactic transfer system for English-French translation. The TAUM project (Traduction Automatique de l'Université de Montréal) had two major achievements: firstly, the Q-system formalism for manipulating linguistic strings and trees (later developed as the Prolog programming language), and secondly, the Météo system for translating weather forecasts. Designed specifically for the restricted vocabulary and limited syntax of meteorological reports, Météo has been successfully operating since 1976 (since 1984 in a new version). An attempt to repeat this success with another sublanguage, that of aviation manuals, failed to overcome the problems of complex noun compounds and phrases, and TAUM ended in 1981.
          in Histoire, Epistemologie, Langage XXII(1) 2001 view details
  • Library of Congress Subject Headings P76 view details
          in Histoire, Epistemologie, Langage XXII(1) 2001 view details