Logic programming is a style of programming that is heavily influenced by formal logic. When Alonzo Church developed the lambda calculus in the 1930s, he also used mathematical logic to show and run computer programmes. However, Cordell Green came up with the idea of using the clausal form of logic to show computer programmes. It can be traced back to debates in the late 1960s and early 1970s about whether to use declarative or procedural representations of knowledge in AI. Moreover, researchers are using logic programming to allow machines to reason because it is useful for information representation. 

Logic Programming Applications

• parsing
• relational database management system
• expert system
• natural language processing solving
• symbolic equation solving
• planning
• prototyping
• simulation
• programming language implementation

The satplan algorithm, for example, makes use of logic to help with planning, while inductive logic programming helps with learning. For example, logic can be used to solve problems and show knowledge. It can also be used for many other things.

What are the types of Logic Programming?

A programme written in a logic programming language is a collection of logically constructed sentences expressing facts and rules about a particular problem domain. The following are three major families of logic programming languages.

• Prolog, 
• answer set programming (ASP), and 
• Datalog 

Types of logic

• Default logic
• non-monotonic logic, and 
• circumscription 

These types of logic are intended to aid in the process of default reasoning and qualification.

Research in AI employs different types of logic.

• Truth functions such as "or" and "not" are used in propositional logic.
• Quantifiers and predicates are used in first-order logic, which lets you talk about things like their properties and relationships to each other.
• There is a "degree of truth" (between 0 and 1) for vague statements like "Alice is old" (or rich, or tall, or hungry), which are too vague linguistically to be either true or false.

How to use it?

For computer programmes to use logic programming, there must be a basis of existing logic, known as predicates. Predicates are used to construct atomic formulas or atoms that express true facts. To create formulas and run queries, predicates and atoms are used. In order to display relevant data, logic languages frequently rely on queries. These queries could be part of a machine learning system that runs without the need for human intervention. 

What is prolog programming?

There are numerous logic programming languages available. Prolog (from the French programmation en logique, or logic programming), the most widely used language, can also communicate with other programming languages such as Java and C. Apart from being the most widely used logic programming language, Prolog was also one of the first, with the first prologue programme being written in the 1970s for use with interpretations. Additionally, Prolog makes use of AI to aid in the formation of its conclusions and is capable of rapidly processing large amounts of data. During data processing, Prolog can be set up to run on its own. This means that it can be set up to run without any input from the user.

Logic programming, particularly Prolog, can assist businesses and organizations in the following ways:

Natural language processing: NLP allows humans and computers to communicate more effectively. NLP is able to listen to human speech in real time, process it, and translate it for computers. This enables AI to "understand" natural language. NLP, on the other hand, is not limited to spoken language. Instead, NLP can be used to read and understand documents, whether they are printed or generated by word processing programmes. NLP is used by Amazon, Alexa and Google Home to process and understand spoken instructions, as well as by email clients to filter spam and warn of phishing attempts.

Database management: NoSQL databases can be created, maintained, and queried using logic programming. Big data can be turned into databases using logic programming. The programming can determine which data has been programmed as important and store it in the appropriate location. 

Predictive analysis: Logic languages can look for inconsistencies or areas of differentiation in large datasets in order to make predictions. This can be useful for identifying potentially dangerous activities (like riding a bike in the middle of a thunderstorm) or predicting industrial machine failures. It can also be used to analyze photographs and make predictions about them.

Conclusion

Numerous extensions to logic have been developed to address specific domains of knowledge, including 

• description logics; 
• situation calculus, 
• event calculus, 
• fluent calculus (for representing events and time); 
• causal calculus; 
• belief calculus (for revising beliefs); and 
• modal logics. 

In logic programming, each programme must have a specific goal. Moreover, to solve a problem, logic programming employs facts and rules. Hence, they are referred to as "building blocks." Likewise, a clausal structure is a subset of first-request predicate logic used in computer programmes. For any computational problem, the first-request logic is well-known and ready. Additionally, the goal-surmising framework controls information needed to prove hypotheses in clausal-structure logic.