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• 正解だと思う選択肢: B

  Reinforcement learning: is the most suitable strategy for a chatbot to continuously improve its responses based on real-time feedback from users. The chatbot can "learn" by receiving positive reinforcement (reward) when it provides a helpful response and negative reinforcement when it doesn't, allowing it to adjust its responses over time to better suit customer needs. Why other options are not suitable: A. While this can provide a good initial training set, it wouldn't allow the chatbot to adapt to new situations or customer feedback without manual intervention. C. This can be helpful in understanding customer patterns but wouldn't directly improve the chatbot's responses without additional training data or feedback mechanisms. D. While updating the FAQ database can be beneficial, it still requires manual effort and wouldn't enable the chatbot to learn from real-time interactions with customers in the same way that reinforcement learning does.

  👍 3

  RightAnswers2024/12/28
• 正解だと思う選択肢: B

  Reinforcement learning: This method allows the chatbot to learn from the outcomes of its actions, essentially receiving "rewards" for positive customer feedback and adjusting its responses accordingly to maximize those rewards in the future.

  👍 2

  aws4myself2024/12/03
• 正解だと思う選択肢: B

  B. Reinforcement learning with rewards for positive customer feedback is the strategy that provides self-improvement capability. In reinforcement learning (RL), an agent (in this case, the chatbot) learns by interacting with its environment and receiving feedback (rewards or penalties). The chatbot can improve its performance over time by adjusting its responses based on positive feedback from users. This allows it to "learn" from past interactions and improve autonomously.

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  Jessiii2025/02/11