Fallbasiertes Schließen (CBR)
Fallbasiert reasoning (CBR) is a problem-solving paradigm in künstliche Intelligenz 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.
Bei CBR wird ein neues Problem mit einem 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.
- Abruf: The system searches for past cases that are similar to the current situation. This often involves using metrics für Ähnlichkeitsbewertungen, wie Distanzmaße.
- Wiederverwendung: The solution from the retrieved case is applied to the new problem, either directly or with modifications to better suit the current context.
- Überarbeitung: The proposed solution is tested and possibly adjusted based on feedback, ensuring that it effectively solves the new problem.
CBR ist besonders nützlich in Bereichen, in denen Wissen complex and difficult to encode into rules, such as medical diagnosis, Kundenservice, 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 Problemlösung in KI, emphasizing the value of learning from experience.