Page 47 - Ai V2.0 Flipbook C8
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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.
Now, let’s explore another type of learning.
Instead of explicitly programming rules such as "if this happens, then do that", we let the system
discover patterns or rules by itself that help solve the problem.
Stages of AI Project Cycle 45

