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Decision Making in Machines/Computers

In our daily lives, we make decisions all the time — for example, choosing what to eat for breakfast, how to reply to a
friend’s message, or which road to take while going to school. These decisions usually depend on our preferences, past
experiences, and the information available to us.
Machines and computers also make decisions, but in a different way. Instead of personal preferences, they rely on
instructions given by humans, logical rules, or data analysis. Sometimes, with the help of Artificial Intelligence, they can
even learn from patterns in data to improve their choices. This process is called decision making in machines.
Decision making in machines means selecting an output or action based on the given inputs, rules, or data. Just like
humans can make both simple and complex decisions, machines too can handle both types — depending on how they
are programmed or trained.

Importance of Decision Making in Computers and Machines
Decision-making in machines enables automation, reduces human intervention, and improves efficiency. It helps machines
perform complex tasks like navigation, monitoring, and problem-solving in real time. For example, in manufacturing,
robots make split-second decisions to avoid collisions and optimise production. Similarly, AI-powered personal assistants
prioritize tasks, manage schedules, and provide useful suggestions to users. This capability is foundational for intelligent
systems that interact with humans and the environment autonomously.

Types of Decision Making

Rule Based Logic Based Learning Based Data Driven

Rule-Based Decision Making

In this method, the machine follows fixed rules or instructions written by humans. These rules never change unless
reprogrammed. The output is always predictable.

Some examples of rule-based decision making are as follows:
u A calculator that always applies arithmetic rules.
u A digital thermometer that shows temperature based on standard conversion formulas.
u Traffic signals that change lights at fixed time intervals.
Some advantages of rule-based decision making are as follows:

u Simple and easy to design.
u Reliable — always gives the same result.
u Very accurate for fixed tasks.
u Requires no large data.

Some disadvantages of rule-based decision making are as follows:
u Cannot adapt to new or changing situations.
u Limited flexibility — works only within the given rules.
u Cannot handle uncertainty.
u Needs reprogramming if conditions change.

Basic Concepts of Artificial Intelligence 39