Learn PROLOG with Real Code Examples

Prolog uses facts, rules, and queries to express logical relationships.

It relies on a built-in inference engine to solve queries automatically.

Ideal for AI, expert systems, and symbolic computation tasks.

Facts, rules, and queries

Horn clauses for logical statements

Recursion for complex relationships

Pattern matching via unification

Backtracking for automatic solution search

Facts: basic knowledge statements

Rules: conditional logic statements

Queries: questions posed to the knowledge base

Variables: placeholders in patterns

Recursion and list processing

src/ - Prolog knowledge base files

tests/ - queries for verification

modules/ - reusable Prolog modules

examples/ - sample AI applications

docs/ - documentation of rules/facts

Write facts and rules in a .pl file

Load file into Prolog interpreter

Pose queries to test logic

Debug using trace and print statements

Refactor knowledge base for modularity

Beginner: simple facts, queries, and rules

Intermediate: recursion, lists, and predicates

Advanced: constraint logic programming, NLP parsing

Expert: AI reasoning engines, theorem proving

Research: advanced symbolic AI systems

More declarative than imperative languages like Python or Java

Stronger logic inference than traditional SQL

Better for symbolic reasoning than C/C++

Less performant for numerical or low-level tasks

Specialized for AI and logic-based applications

1972 – Initial development by Colmerauer and Kowalski

1980s – ISO standardization discussions

1983 – Edinburgh Prolog became popular

1990s – SWI-Prolog and GNU Prolog developed

2000s+ – Modern Prolog interpreters with libraries and web integration

Fact: Atomic statement of truth

Rule: Conditional logical statement

Query: Question posed to the knowledge base

Unification: Pattern matching process

Backtracking: Automatic search of alternatives