AI02 Intelligent Agents: Structure, Environments, and Rationality
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AI02 Intelligent Agents: Structure, Environments, and Rationality
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The provided texts comprehensively introduce the concept of intelligent agents in artificial intelligence, defining them as entities that perceive environments through sensors and act via actuators. They explain how an agent function abstractly maps percept sequences to actions, while an agent program concretely implements this, contrasting it with the impractical table-driven approach. A central theme is rationality, which dictates agents should choose actions to maximize a performance measure, emphasizing the critical importance of its correct formulation. The sources categorize task environments using the PEAS framework (Performance, Environment, Actuators, Sensors) and classify them by properties like observability, determinism, and episodic nature. Finally, they detail different agent architectures—simple reflex, model-based reflex, goal-based, and utility-based agents—progressing in complexity, and highlight the crucial role of learning agents with their performance element, learning element, critic, and problem generator in achieving autonomy and adapting to unknown environments.