ケースベース推論（CBR）
ケースベース reasoning (CBR) is a problem-solving paradigm in 人工知能 that utilizes past experiences to address new challenges. The core idea behind CBR is to leverage historical cases—essentially, previous instances of problems and their solutions—to inform decisions in similar situations.
CBRでは、新しい問題を既存のケースと比較する database of previously solved cases. The system retrieves the most relevant cases based on their similarity to the current problem. After identifying these relevant cases, the system can adapt their solutions to fit the specifics of the new problem. This process typically involves three main steps: retrieval, reuse, and revision.
- 検索： The system searches for past cases that are similar to the current situation. This often involves using metrics 類似性評価のために、距離測定などを用いる。
- 再利用： The solution from the retrieved case is applied to the new problem, either directly or with modifications to better suit the current context.
- 修正： The proposed solution is tested and possibly adjusted based on feedback, ensuring that it effectively solves the new problem.
CBRは、知識が complex and difficult to encode into rules, such as medical diagnosis, カスタマーサービス, and legal advice. The method offers several advantages, including the ability to learn from new cases over time and improving decision-making through continuous experience accumulation.
However, CBR also comes with challenges, such as the need for a well-organized case library and ensuring the relevance of past cases to new problems. Overall, case-based reasoning represents a practical and intuitive approach to AIにおける問題解決, emphasizing the value of learning from experience.