Hybrid Systems

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Hybrid systems combine different AI approaches into one solution so they can work better together. A common setup mixes machine learning with rule-based or logical reasoning. Machine learning is used to recognize patterns or make sense of data, while rules help control decisions and explain why certain actions were taken.

Hybrid systems are often used in areas like robotics and safety-sensitive applications, where relying on machine learning alone can be risky. Designing a hybrid system means deciding which parts should learn from data and which parts should follow clear rules. When this is done well, hybrid systems become more reliable for real-world use where both adaptability and transparency are important.

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