Artificial Intelligence: A Modern Approach (3rd Edition)
Stuart Russell and Peter Norvig,
[Download Book]
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the
full breadth of the field, which encompasses logic, probability, and continuous mathematics;
perception, reasoning, learning, and action; and everything from microelectronic devices to
robotic planetary explorers. The book is also big because we go into some depth.
The subtitle of this book is “A Modern Approach.” The intended meaning of this rather
empty phrase is that we have tried to synthesize what is now known into a common framework,
rather than trying to explain each subfield of AI in its own historical context. We
apologize to those whose subfields are, as a result, less recognizable.
full breadth of the field, which encompasses logic, probability, and continuous mathematics;
perception, reasoning, learning, and action; and everything from microelectronic devices to
robotic planetary explorers. The book is also big because we go into some depth.
The subtitle of this book is “A Modern Approach.” The intended meaning of this rather
empty phrase is that we have tried to synthesize what is now known into a common framework,
rather than trying to explain each subfield of AI in its own historical context. We
apologize to those whose subfields are, as a result, less recognizable.
The main unifying theme is the idea of an intelligent agent. We define AI as the study of
agents that receive percepts from the environment and perform actions. Each such agent implements
a function that maps percept sequences to actions, and we cover different ways to
represent these functions, such as reactive agents, real-time planners, and decision-theoretic
systems. We explain the role of learning as extending the reach of the designer into unknown
environments, and we show how that role constrains agent design, favoring explicit knowledge
representation and reasoning. We treat robotics and vision not as independently defined
problems, but as occurring in the service of achieving goals. We stress the importance of the
task environment in determining the appropriate agent design.
agents that receive percepts from the environment and perform actions. Each such agent implements
a function that maps percept sequences to actions, and we cover different ways to
represent these functions, such as reactive agents, real-time planners, and decision-theoretic
systems. We explain the role of learning as extending the reach of the designer into unknown
environments, and we show how that role constrains agent design, favoring explicit knowledge
representation and reasoning. We treat robotics and vision not as independently defined
problems, but as occurring in the service of achieving goals. We stress the importance of the
task environment in determining the appropriate agent design.
To download the book, click on the following links
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