Download Artificial Intelligence: Foundations of Computational Agents by David L. Poole, Alan K. Mackworth PDF

By David L. Poole, Alan K. Mackworth

Fresh many years have witnessed the emergence of man-made intelligence as a major technological know-how and engineering self-discipline. synthetic Intelligence: Foundations of Computational brokers is a textbook aimed toward junior to senior undergraduate scholars and first-year graduate scholars. It provides man made intelligence (AI) utilizing a coherent framework to review the layout of clever computational brokers. by way of exhibiting how uncomplicated ways healthy right into a multidimensional layout area, readers can study the basics with no wasting sight of the larger photograph. The booklet balances thought and test, exhibiting tips to hyperlink them in detail jointly, and develops the technological know-how of AI including its engineering purposes.

Although based as a textbook, the book's user-friendly, self-contained type also will attract a large viewers of execs, researchers, and self reliant inexperienced persons. AI is a speedily constructing box: this ebook encapsulates the most recent effects with out being exhaustive and encyclopedic. It teaches the most ideas and instruments that would permit readers to discover and research on their lonesome.

The textual content is supported via an internet studying surroundings, artint.info, in order that scholars can test with the most AI algorithms plus difficulties, animations, lecture slides, and an information illustration method for experimentation and challenge fixing.

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Extra resources for Artificial Intelligence: Foundations of Computational Agents

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Given a well-defined problem, the next issue is whether it matters if the answer returned is incorrect or incomplete. For example, if the specification asks for all instances, does it matter if some are missing? Does it matter if there are some extra instances? Often a person does not want just any solution but the best solution according to some criteria. There are four common classes of solutions: Optimal solution An optimal solution to a problem is one that is the best solution according to some measure of solution quality.

Booking tickets, getting to the departure airport, getting to the destination airport, and getting to the holiday location). In a hierarchical representation, the agent will solve these subtasks in a hierarchical way, until the problem is reduced to simple problems such a sending an http request or taking a particular step. A hierarchical decomposition is important for reducing the complexity of building an intelligent agent that acts in a complex environment. However, to explore the other dimensions, we initially ignore the hierarchical structure and assume a flat representation.

How can the problem be represented? What distinctions in the world are needed to solve the problem? What specific knowledge about the world is required? How can an agent acquire the knowledge from experts or from experience? How can the knowledge be debugged, maintained, and improved? • How can the agent compute an output that can be interpreted as a solution to the problem? Is worst-case performance or average-case performance the critical time to minimize? Is it important for a human to understand how the answer was derived?

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