Reactive machines are artificial intelligence models that continuously interact with their environment, without maintaining an internal representation of it. These models rely on rules and heuristics to make real-time decisions and adjust to changing environmental conditions. Reactive machines are particularly useful in real-time applications, such as robot control systems and autonomous navigation systems, as they can make fast and accurate decisions without the need to process large amounts of data or maintain long-term memory. However, reactive machines are limited in their ability to learn from experience and adapt to new and unfamiliar situations. They are therefore used in combination with other machine learning approaches, such as reinforcement learning and supervised learning, to improve their adaptive and decision-making capabilities.
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