Logical programming languages | what should you know?

Programming paradigms are defined as “styles or ways of programming.” Programming languages are often divided on the basis of programming paradigms. Some of the most used programming paradigms are object-oriented programming, procedural programming, functional programming, and logic programming.

In its simplest definition, logical programming is a way of programming that highly depends on formal logic. Logic itself is defined as a field that seeks the truth and reasoning. Informal logic tries to characterize valid and logical arguments informally, while formal logic represents statements and patterns symbolically.

The model of logic programming uses the latter type of logic in its functioning. The statements in logical programming find to express the facts and rules about the problems within the system of this formal logic. Logical programming finds usage in some of the most important fields of computer science, such as artificial intelligence.

All logical programming languages are declarative languages, but some are a combination of declarative programming language paradigm and other programming paradigms. For example, logical languages such as answer set programming (ASP) and Datalog and purely declarative, while other languages such as Prolog are a combination of declarative and imperative programming.  


Logical programming in its modern form started in the 1960-the 70s when procedural and declarative programming was used to present knowledge in the field of artificial intelligence. It was at this time when, Planner, the first programming language within this procedural paradigm, came into being at MIT.

Pat Hayes and Robert Kowalski, two computer scientists from the UK, combined this procedural approach to knowledge representation with logic-based declarative. Robert Kowalski’s ideas later helped Colmerauer, another computer scientist, in the development of the logic programming language Prolog, which was based on first-order logic (also known as predicate logic or first-order predicate logic), a formal logic in the field of mathematics, philosophy and computer science.

Prolog programming language’s formation further resulted in the development of various other logical languages such as Visual Prolog, Ciao, Fril, ALF, as well as constraint logic programming languages such as Datalog.

Working mechanism:

Logic programming is a programming paradigm that is based on logic. This means logic programming languages have statements that follow logic by expressing facts and rules.

Computer programs in logic programs are made with the help of logical inferences that are based on the available data to the program. The program also uses a set of existing logic as a base which is called a predicate. Predicates are basically the units in logic programming that express facts-the basis of logic programming.

An algorithm in logic programming paradigm:

An algorithm is a logical programming paradigm that is a combination of a control component and a logic component. A program is usually broken down into these two components. While the control component is optional, the logic component is the essence of the algorithm.

In purely logic-based programs (i.e., the programs that do not use control components), the logic component alone decides the solution. In a program where both the components are employed, the control component determines the ways by which the solution is produced.

Variants of logic programming:

While the basic features of the logic programming paradigm are similar for all logic programming languages, there are some variations that significantly affect the running of the program.

Some important variants of logic programming are listed below:


Prolog is a prominent logic programming language that emerged in 1972 as a result of a collaboration between Kowalski and Colmerauer. Prolog uses a formal logic named first-order logic. First-order logic uses quantified variables in logical statements to define an expression.

In prolog programming language, the logic of the program is expressed in terms of mathematical relations, which represent the rules and facts on which the program runs. Computations are performed by running queries on these relations.

Constraint logic programming

Constraint logic programming is a special subtype of logic programming and constraint programming in which constraint satisfaction is achieved. Constraint satisfaction is a process by which solutions are found for a set of constraints that impose certain conditions on variables. So, constraint is just normal programming with just constraints on the body of clauses.

Constraint logic programming is used in engineering, automated planning and scheduling, and type inference, among other fields. Prolog can support constraint programming.

Concurrent logic programming

Concurrent logic programming integrates logic programming with concurrent programming. In this programming, the programs are sets of horn clauses in a specific form. It is not very widespread in its usage in practical problem solving and is mainly studied for research purposes in the field of computer science and mathematics.

Concurrent constraint logic programming

Concurrent constraint logic programming integrates constraint logic programming with concurrent logic programming, where concurrent programming processes are emphasized more than solving constraint satisfaction problems.

Similar to the concurrent logic programming paradigm, concurrent constraint logic programming is also mainly studied for research purposes instead of being used in the practical world.

Abductive logic programming

Abductive logic programming, as the name suggests, uses abductive reasoning to solve problems declaratively. Abductive reasoning is a type of reasoning in logic and philosophy which, unlike deductive reasoning, gives an uncertain result. Consequently, abductive logic programming allows some predicates (base logic) to be incompletely defined. These are call abducible (non-comparable) predicates.

Problem-solving in abductive logic programming paradigm is achieved by deriving hypotheses on abducible predicates, which may result in observation to be explained (in the case of research work) or goals to be achieved (in the case of practical problem-solving processes).

Abductive logic programming is used as a problem solver in various fields, including planning, machine learning, and natural language processing.

Metalogic programming

Logic programming can also be used to perform metalevel programming. Metaprogramming is basically defined as a type of programming in which one program uses another program or a group of programs as objects and to read, analyze and manipulate other programs or even itself while running.

Metalogic programming allows object-level and meta-level representations to be combined together for use. Metalogic programming helps implement metaprograms which are further used to manipulate other programs and databases. For these reasons, metalogic programming is used in databases and natural language and databases.  

Inductive logic programming

Inductive is a sub field of artificial intelligence. As the name suggests, it uses inductive reasoning in its work. Inductive reasoning is the type of reasoning in which a theory or hypothesis is built to explain an observed fact.

Inductive logic programming uses logic programming as the background to a program and gives the logical consequences of the background logic given to it. It is very useful in the fields of artificial intelligence (particularly in natural language processing) and in bioinformatics.

The above-mentioned variants of logic programming are some of the most important ones as most of them are used in practical problem solving and for academic research purposes. There may be more sub-types of logic-based programming apart from these types.


While logic programming languages may not be very effective in number crunching, they are quite capable of transforming logical statements in computer programs. For example, they can determine a correct logical statement that should succeed the original logical statements given. For these reasons, logic programming languages are widely used in many businesses and research fields.

Some of the fields in which logic programming is most used are given below:

Artificial intelligence

By far, the field that most relies on logical programming is artificial intelligence. It is defined as the intelligence observed in machines such as computers, unlike the natural intelligence which is present in living beings.

This programming language is widely used in the field of artificial intelligence. For example, prolog programming is used to determine whether or not a new statement is logically correct based on the already given statements. Because of these capabilities, the programming language Prolog is widely used in artificial intelligence.

Natural language processing (NLP)

Natural language processing is a sub-type of artificial intelligence concerned with human-computer interaction. NLP mainly tries to improve the machine or computer’s capability to understand natural language data and generate this type of data on its own.

NLP also allows converting the human-understandable spoken natural language data into the computer under stable language. Furthermore, it will allow computers to understand written natural language data such as documents and identity cards and analyze, sort, and use it according to our needs.

Natural language programming is already in use by major high-tech companies in the world and is allowing for higher efficiencies in business processes.

Big Data processing

Logic programming languages are also suited to work with large amounts of data because they use artificial intelligence processes in their running.

Prolog is a common logic programming that uses artificial intelligence in a practical sense. Prolog language is based on first-order predicate logic, which allows the use of variables in programming rather than propositions. Prolog is able to process large amounts of data in a limited amount of time which is why it is perfectly suited to work with big data.

Predictive analytics

Predictive analytics is a sub-field of statistics that uses current and historical data to make predictions about the future. Predictive analytics find usage in a variety of fields, including finance, marketing, telecommunication, and social networking, among others.

Logic languages can easily detect patterns and anomalies in large datasets that can be used to make important predictions. This capability of logic programming languages allows them to find use in predictive analytics. 

Database management

Logic programming also finds usage in database management. They can be used to create databases out of raw unstructured big data. They can also be used to easily sort, maintain, and query data or even analyze the data for results. It is only possible because logic programming languages can identify which information is relevant for what purpose.

For example, you can use a computer program in a logical programming language to analyze what is the best route to get to Washington DC-based on all the data it has. The computer program will quickly run all the relevant analyses and show you the result.

Deductive Databases

Deductive databases are the database systems that allow a user to get data on the basis of rules or facts stored in them. As the primary concept on which logic programming is built upon is programming by rules and facts, logic programming is quite useful in deductive databases.

The most important language used in a deductive database system is Datalog, which has seen a recent usage surge in its usage in the past years in the field of cloud computing, security, and networking.

Theorem proving

Theorem proving uses mathematical logic to prove mathematical theorems. Automated theorem proving takes the process one step ahead and makes the process automated with the help of computer programs.

First-order logic used in automated theorem proving processes. The first-order logic model is effective in this type of problem-solving because it allows problems to be expressed in an intuitive, natural, stable, and solvable way.

Pattern matching

Due to the logic and fact-based nature of logic programming languages, they find usage in pattern matching. In computer science, pattern matching is basically just patterns recognition except that in pattern matching, there is an exact sequence that needs to be repeated for it to be qualified as a pattern, unlike pattern recognition, where only similarities are enough for a pattern to qualify.


Logic programming is a programming paradigm that is based on formal logic and uses facts and rules in its operations. Logical programming languages have a number of variants that are specialized for their specific purposes. Due to the logic-oriented nature of logical programming languages, they are widely used in the field of artificial intelligence (NLP) and database management. 

Let’s have a look at some other MAJOR Programming Paradigms in detail:

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