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What is the primary function of a rule-based system as described in Week 2?

• Handle uncertainty in customer preferences
• Use if-then rules for decisions based on predefined conditions
• Simulate dynamic customer interactions
• Learn from past cases to adapt solutions

In Week 2, what is fuzzification in the context of fuzzy logic?

• Converting fuzzy outputs into crisp values
• Applying if-then rules to make decisions
• Converting crisp numerical inputs into fuzzy linguistic values
• Optimizing strategies using evolutionary computation

What is an example of a rule-based system application for customer decisions in Week 2?

• Predicting churn using neural networks
• Sending a discount email if cart value > $50 and no purchase in 10 minutes
• Simulating agent interactions in a virtual environment
• Using past cases to recommend products

According to Week 2, what is a potential ethical issue with rule-based systems?

• They are always fair and unbiased
• Strict rules can lead to unfair assumptions, e.g., low purchases mean disinterest
• They handle uncertainty better than fuzzy logic
• They dynamically adapt to new situations

What does defuzzification do in fuzzy logic as per Week 2 notes?

• Converts crisp inputs into fuzzy values
• Applies fuzzy rules to produce unclear outputs
• Converts fuzzy control signals into a crisp control signal
• Simulates customer interactions over time

What is the main purpose of Case-Based Reasoning (CBR) as described in Week 3?

• Use fixed if-then rules for customer decisions
• Simulate dynamic customer interactions
• Solve new problems by retrieving and adapting past cases
• Handle uncertainty with fuzzy logic

In the CBR cycle from Week 3, what does the "Retrieve" step involve?

• Testing and refining solutions in real-time
• Storing new cases for future use
• Finding the most similar past case from a case base
• Adapting a solution from a past case

According to Week 3, what is an advantage of CBR over rule-based systems?

• It is less flexible and relies on rigid rules
• It adapts to new situations without predefined rules
• It uses binary true/false logic
• It requires a complex neural network

What is an ethical risk highlighted in Week 3 for CBR in customer behavior modeling?

• Biased profiling, e.g., discounts only for certain demographics
• Using fixed rules for all decisions
• Generating synthetic data without privacy concerns
• Avoiding all data tracking

In the Week 3 cart abandonment case study, what is a CBR application example?

• Simulating agent interactions in a retail environment
• Reducing purchase likelihood by offering free shipping based on past cases
• Using fuzzy logic to handle uncertain preferences
• Optimizing promotions with evolutionary computation