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to simulate human thinking and problem-solving abilities. By developing these models, we enable
AI systems to perform tasks that require reasoning, understanding and adaptability.
AI modelling techniques can be broadly classified into two approaches, namely, rule-based and
learning-based.

Rule-Based AI

Rule-based approach is based on a set of rules and a set of facts already fed to the machine to
generate the desired output. These models can operate with simple basic information and data.
The relationship or patterns in the data is defined by the developer.
IF X happens THEN do Y

Examples of rule-based AI
Example 1: If IT RAINS Then TAKE AN UMBRELLA
Example 2: If TRAFFIC LIGHT TURNS RED Then STOP THE CAR

Let us understand the concept of a Rule-Based AI system using a given scenario.
You have a dataset containing 100 images of chairs and 100 images of tables. To train your
machine, you provide this data and label each image as either a chair or a table. When you test
the machine with a new image of a chair, it compares this image to the labelled training data and
identifies it as a chair based on those labels. This method is known as a rule-based approach,
where the rules are the labels assigned to the training data.

Rule-based Approach

Labelled Datasets

Model
Used to Train Machine Used to Test Machine

Output Training Data

Machine Identifies the Image as Table Testing Data

Based on various guidelines, we can make different choices.

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