# Predicate Logic in Artificial intelligence with Example

## Predicate Logic

An expression of one or more variables that have been determined on a certain domain is called a predicate. By giving the variable a value or by quantifying it, a predicate with variables can be turned into a proposition.

Propositional logic is insufficient to describe real language statements or complex phrases. Consequently, first-order predicate logic, or FOPL, is needed. It is an effective language for conveying details about an object and the relationship between them.

In artificial intelligence, one of the most significant and ancient knowledge representation systems is predicate logic, sometimes known as first-order predicate logic (FOPL).

### Predicate Logic  Components in Artificial Intelligence

• Predicate Symbols
• Constant Symbols
• Variable Symbols
• Function Symbols

#### 1. Predicate Symbols

These serve as a domain's representation of a relation. For instance, the predicate symbol write is used to indicate a sentence such as "Raju learn chapter".

The straightforward formula is going to be:

Study up (Raju, chapter).

#### 2. Constant Symbols

In a domain, items or entities are represented by constant symbols. These things or entities could be tangible things, real persons, ideas, or anything that needs a name. For instance, Suman and books are constant symbols in the calculation above.

#### 3. Variable Symbols

X and Y, which are variable symbols, are also words. They provide users the freedom to choose any entity they want. As an illustration, Put (x, y) in writing.

#### 4. Function Symbols

They indicate roles inside the conversation's domain. The atomic formula, for instance, might be MARRIED[mother (Raju), father (Ravi)], where mother and father are function symbols. Raju's mother and father are married.

### Examples of Predicate Logic in Artificial Intelligence

To illustrate the idea that Raju and Kajal are classmates.

classmates (Kajal and Raju)

Twins(Kajal and Ramesh)

• Propositional logic, first-order logic is more expressive and can represent intricate relationships and concepts.
• Because first-order logic may employ quantifiers and variables, it is frequently more effective.
• first-order logic is simpler to deal with and reason about because of its clearly stated semantics.

• Because first-order logic is more complicated than propositional logic, it is harder to understand and use.
• Because first-order logic frequently requires reasoning about quantifiers and variables, it is also less tractable than propositional logic in many situations.
•  Applying first-order logic in real-world situations can be challenging since different applications require different assumptions and rules.