A company wants its customer service chatbot to improve over time by learning from past interactions and feedback. Which learning approach supports this kind of self-improvement?

Reinforcement learning is the most suitable approach because it allows an agent (the chatbot) to learn optimal behaviors through trial and error by maximizing a reward signal. In this scenario, positive customer feedback serves as the reward, guiding the chatbot to improve its responses over time without explicit programming for every possible interaction. Supervised learning with a manually labeled dataset would require continuous human labeling of new interactions, which is not scalable for ongoing self-improvement. While a continuously updated FAQ database could be used with supervised learning, it primarily focuses on retrieving information rather than learning to generate improved, nuanced responses based on feedback. Unsupervised learning discovers patterns but doesn't inherently guide the chatbot toward better performance based on external feedback.