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Logic programming is a declarative programming paradigm in which a set of attributes that a solution should have are specified rather than set of steps to obtain such a solution. The most widely used logic programming language is Prolog. Other, more modern examples of the paradigm include Mercury, Visual Prolog and Oz. Schematically, the process is
- facts + rules = results.
A related paradigm is inductive logic programming, which is concerned with finding general rules based on a sample of facts.
The point of logic programming is to bring the style of formal logic to computer programming. Mathematicians and philosophers find logic a successful tool for developing bodies of theory. Many problems are naturally expressed as a theory. To say a problem needs solving is often equivalent to asking if a new hypothesis is consistent with an existing theory. Logic provides a way to prove whether the question is true or false. The process of constructing a proof is well-known, so logic is thought to be a reliable way to answer questions. Logical programming systems automate this process. Artificial Intelligence was an important influence on the development of logic programming.
The monkey and banana problem is a famous problem studied in the logic programming community. Instead of the programmer explicitly specifying the path for the monkey to reach the banana, the computer actually reasons out a possible way that the monkey reaches the banana.
Logic programming creates logical models that describe the world in which a problem exists. The logic programming approach is to create new statements about its model. The knowledge of the state of the world is expanded each time. A problem is typically stated as a single hypothesis. The logic program solves the problem by attempting to prove whether the hypothesis is actually a theorem about the model.
Some popular application domains for logic programming are:
- Expert systems, where the program generates a recommendation or answer from a large model of the application domain.
- Automated theorem proving, where the program generates novel theorems to extend some existing body of theory.
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