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u Wrong or biased data can give wrong results.
u Requires high processing power.
u Raises privacy and security concerns.

Case Study: E-Commerce Recommendations

Online shopping platforms like Amazon use data-based decision making. If you buy or search for shoes, the website
shows suggestions like socks, laces, or similar shoes. This improves customer experience and increases sales. However, if
the data is incomplete or inaccurate, the recommendations may not be useful, sometimes showing irrelevant products.

Learning-Based Decision Making

Machines use Artificial Intelligence (AI) and Machine Learning (ML). Instead of only following rules, they learn from past
experiences or massive data. Over time, their decision-making ability improves.

Some examples of learning-based decision making are as follows:
u Voice assistants like Siri, Alexa, or Google Assistant.
u Self-driving cars that learn traffic patterns.
u Google Translate improving translations with user feedback.
Some advantages of learning-based decision making are as follows:
u Learns and improves over time.
u Can adapt to new situations.

u Handles uncertainty better than other methods.
u Useful for complex, real-life problems.
Some disadvantages of learning-based decision making are as follows:
u Expensive to build and maintain.

u Requires huge amounts of training data.
u May develop biases if trained on biased data.
u Raises ethical concerns (job loss, privacy).

Case Study: Self-Driving Cars

Self-driving cars use learning-based decision making. They collect data from sensors (speed, distance, road conditions)
and learn patterns to decide when to brake, accelerate, or change lanes. This reduces human errors and accidents.
However, challenges like unexpected pedestrian behaviour or bad weather can make decisions harder for the AI,
showing its current limitations.

Why is Decision Making Important in AI?
Decision making is at the heart of Artificial Intelligence (AI). Without decision making, AI would only follow fixed instructions
like a calculator. What makes AI powerful is its ability to choose the best action from many possibilities, often in uncertain
or changing conditions.
u To automate complex tasks: AI helps in automating tasks that are too complex for simple programming.
Example: A self-driving car must decide whether to stop, slow down, or overtake another vehicle. This requires
continuous decision making, not just fixed rules.
u To handle uncertainty: Real-life situations are uncertain. AI decision making helps machines analyse incomplete or
changing information and still act.
Example: A medical AI system can suggest treatment even if all patient data is not available.

Basic Concepts of Artificial Intelligence 41