Prolog is a high-level programming language that has played a foundational role in artificial intelligence, particularly in the area of logic programming. Its name stands for Programming in Logic. Prolog was created in the early 1970s by Alain Colmerauer and colleagues as a tool for natural language processing, but it quickly became a general-purpose language for a wide range of AI tasks. Unlike most other programming languages that are procedural or object-oriented, Prolog is declarative. This means you describe the problem and relationships as a set of facts and rules, and the Prolog engine figures out how to solve queries based on those relationships.
At the heart of Prolog is the idea of representing knowledge using facts (statements that are unconditionally true) and rules (conditional statements that define relationships between facts). For example, you might encode family relationships by writing facts like parent(alice, bob). and rules like grandparent(X, Y) :- parent(X, Z), parent(Z, Y). The query system then lets you ask questions about the knowledge base, such as “Who is Bob’s grandparent?” Prolog automatically searches for answers by applying logical inference and unification to the facts and rules you provided.
This approach is especially useful in AI applications that require symbolic reasoning, such as expert systems, natural language understanding, theorem proving, and knowledge representation. Prolog’s inference engine uses a method called backward chaining, where it starts with a query and works backwards to see if it can be satisfied by the known facts and rules. This is different from forward chaining, which starts with known data and derives new facts.
One of the key strengths of Prolog is its ability to elegantly express complex relationships and recursively defined problems. For instance, solving puzzles, parsing sentences, or navigating a maze can often be accomplished in just a few lines of Prolog code. Because of its logical foundation, Prolog is an excellent tool for prototyping reasoning systems and exploring how computers can mimic aspects of human logical thought.
While modern AI has shifted much of its focus to data-driven machine learning approaches, Prolog and logic programming remain important for certain types of problems where explicit rules and logical consistency are essential. Prolog also serves as a gateway to learning about more advanced topics such as knowledge-based systems and automated reasoning.
Prolog is widely taught in computer science curricula and is still used in research and some industry applications, particularly where explainability and rule-based reasoning are priorities. Its influence can be seen in other logic-based systems and in the development of knowledge representation languages. If you’re interested in the symbolic side of AI, learning Prolog offers a unique perspective that complements more statistical approaches.
