AI agents make decisions by first perceiving their environment through sensors, then processing this input to form an internal representation of the current state. They use various algorithms, often guided by predefined goals or reward functions, to determine the optimal course of action. For instance, rule-based systems follow explicit if-then logic, while search algorithms explore potential future states to find the best path. More advanced agents leverage machine learning models, particularly reinforcement learning, where they learn optimal strategies through trial and error by maximizing cumulative rewards over time. Ultimately, the agent selects an action that is predicted to best achieve its objectives given the current perceived state and its learned or programmed decision-making logic. More details: https://info-web.top