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Each such agent implements a function that maps percept sequences to actions, and covers different ways to represent these functions, such as reactive agents, real-time planners, and decision-theoretic systems, and explains the role of learning as extending the reach of the designer into unknown environments and show how that role constrains agent design, favoring explicit knowledge representation and reasoning and treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. The importance of the task environment is in determining the appropriate agent design.
Our primary aim is to convey the ideas that have emerged over the past fifty years of AI research and the past two millennia of related work and tried to avoid excessive formality in the presentation of these ideas while retaining precision. .
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