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The field of Artificial Intelligence strives to produce computer programs that exhibit intelligent behavior. One of the areas of interest is the processing of natural language. This report will discuss the role of the computer language PROLOG in Natural Language Processing (NLP) both from theoretic and pragmatic viewpoints. The reasons for using PROLOG for NLP are numerous. First, linguists can write natural language grammars almost directly as PROLOG programs; this allows 'fast-prototyping' of NLP systems and facilitates analysis of NLP theories. Second, semantic representations of natural language texts that use logic formalisms are readily produced in PROLOG because of PROLOG's logical foundations. Third, PROLOG's built-in inferencing mechanisms are often sufficient for inferences on the logical forms produced by NLPs. Fourth, the logical, declarative nature of PROLOG may make it the language of choice for parallel computing systems. Finally, the fact that PROLOG has a de facto standard (Edinburgh) makes the porting of code from one computer system to another virtually trouble free. Perhaps the strongest tie one could make between NLP and PROLOG was stated by John Stuart Mill in his inaugural Address at St. Andrews: The structure of every sentence is a lesson in logic.

The field of Artificial Intelligence strives to produce computer programs that exhibit intelligent behavior. One of the areas of interest is the processing of natural language. This report will discuss the role of the computer language PROLOG in Natural Language Processing (NLP) both from theoretic and pragmatic viewpoints. The reasons for using PROLOG for NLP are numerous. First, linguists can write natural language grammars almost directly as PROLOG programs; this allows 'fast-prototyping' of NLP systems and facilitates analysis of NLP theories. Second, semantic representations of natural language texts that use logic formalisms are readily produced in PROLOG because of PROLOG's logical foundations. Third, PROLOG's built-in inferencing mechanisms are often sufficient for inferences on the logical forms produced by NLPs. Fourth, the logical, declarative nature of PROLOG may make it the language of choice for parallel computing systems. Finally, the fact that PROLOG has a de facto standard (Edinburgh) makes the porting of code from one computer system to another virtually trouble free. Perhaps the strongest tie one could make between NLP and PROLOG was stated by John Stuart Mill in his inaugural Address at St. Andrews: The structure of every sentence is a lesson in logic.

The field of Artificial Intelligence strives to produce computer programs that exhibit intelligent behavior. One of the areas of interest is the processing of natural language. This report will discuss the role of the computer language PROLOG in Natural Language Processing (NLP) both from theoretic and pragmatic viewpoints. The reasons for using PROLOG for NLP are numerous. First, linguists can write natural language grammars almost directly as PROLOG programs; this allows 'fast-prototyping' of NLP systems and facilitates analysis of NLP theories. Second, semantic representations of natural language texts that use logic formalisms are readily produced in PROLOG because of PROLOG's logical foundations. Third, PROLOG's built-in inferencing mechanisms are often sufficient for inferences on the logical forms produced by NLPs. Fourth, the logical, declarative nature of PROLOG may make it the language of choice for parallel computing systems. Finally, the fact that PROLOG has a de facto standard (Edinburgh) makes the porting of code from one computer system to another virtually trouble free. Perhaps the strongest tie one could make between NLP and PROLOG was stated by John Stuart Mill in his inaugural Address at St. Andrews: The structure of every sentence is a lesson in logic.

The field of Artificial Intelligence strives to produce computer programs that exhibit intelligent behavior. One of the areas of interest is the processing of natural language. This report will discuss the role of the computer language PROLOG in Natural Language Processing (NLP) both from theoretic and pragmatic viewpoints. The reasons for using PROLOG for NLP are numerous. First, linguists can write natural language grammars almost directly as PROLOG programs; this allows 'fast-prototyping' of NLP systems and facilitates analysis of NLP theories. Second, semantic representations of natural language texts that use logic formalisms are readily produced in PROLOG because of PROLOG's logical foundations. Third, PROLOG's built-in inferencing mechanisms are often sufficient for inferences on the logical forms produced by NLPs. Fourth, the logical, declarative nature of PROLOG may make it the language of choice for parallel computing systems. Finally, the fact that PROLOG has a de facto standard (Edinburgh) makes the porting of code from one computer system to another virtually trouble free. Perhaps the strongest tie one could make between NLP and PROLOG was stated by John Stuart Mill in his inaugural Address at St. Andrews: The structure of every sentence is a lesson in logic.

Logic Programming can be broadly defined as "using logic to infer computational steps from existing propositions" However, mathematical logic cannot always infer computational steps because computational systems make use of arbitration for determining which message is processed next by a recipient that is sent multiple messages concurrently. Since arrival orders are in general indeterminate, they cannot be inferred from prior information by mathematical logic alone. Therefore mathematical logic cannot in general implement computation. Consequently, Procedural Embedding of Knowledge is strictly more general than Logic Programming. This conclusion is contrary to Robert Kowalski who stated "Looking back on our early discoveries, I value most the discovery that computation could be subsumed by deduction." Nevertheless, logic programming (like functional programming) can be a useful programming idiom. Over the course of history, the term "functional programming" has grown more precise and technical as the field has matured. Logic Programming should be on a similar trajectory. Accordingly, "Logic Programming" should have a general precise characterization. Kowalski's approach has been to advocate limiting Logic Programming to backward-chaining only inference based on resolution using reduction to conjunctive normal form in a global states model. In contrast, our approach is explore Logic Programming building on the logical inference of computational steps using inconsistency-robust reasoning in a configurations model. Because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic, e.g., using resolution theorem proving.

This is a tutorial on logic programming and Prolog appropriate for a course on programming languages for students familiar with imperative programming.

Introduction. The organization of branches and repetitions in the context of logical programming is considered by an example of the Prolog language. The fundamental feature of the program in a logical programming language is the fact that a computer must solve a problem by reasoning like a human. Such a program contains a description of objects and relations between them in the language of mathematical logic. At the same time, the software implementation of branching and repetition remains a challenge in the absence of special operators for the indicated constructions in the logical language. The objectives of the study are to identify the most effective ways to solve problems using branching and repetition by means of the logic programming language Prolog, as well as to demonstrate the results obtained by examples of computational problems.  Materials and Methods. An analysis of the literature on the subject of the study was carried out. Methods of generalization and systematization of knowledge, of the program testing, and analysis of the program execution were used.  Results. Constructions of branching and repetition organization in a Prolog program are proposed. To organize repetitions, various options for completing a recursive cycle when solving problems are given.  Discussion and Conclusions. The methods of organizing branches and repetitions in the logic programming language Prolog are considered. All these methods are illustrated by examples of solving computational problems. The results obtained can be used in the further development of the recursive predicates in logical programming languages, as well as in the educational process when studying logical programming in the Prolog language. The examples of programs given in the paper provide using them as a technological basis for programming branches and repetitions in the logic programming language Prolog.