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

By David L. Poole, Alan K. Mackworth

Contemporary many years have witnessed the emergence of synthetic intelligence as a major technology 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 offers synthetic intelligence (AI) utilizing a coherent framework to review the layout of clever computational brokers. via exhibiting how uncomplicated methods healthy right into a multidimensional layout area, readers can study the basics with no wasting sight of the larger photograph. The publication balances thought and scan, displaying the right way to hyperlink them in detail jointly, and develops the technological know-how of AI including its engineering functions.

Although dependent as a textbook, the book's elementary, self-contained sort also will attract a large viewers of pros, researchers, and self sustaining 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 rules and instruments that may let readers to discover and research on their lonesome.

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

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Extra info for Artificial Intelligence: Foundations of Computational Agents

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In many cases, it is difficult to predict the effect of an action, and the best an agent can do is to have a probability distribution over the effects. For example, a person may not know the effect of calling his dog, even if he knew the state of the dog, but, based on experience, he has some idea of what it will do. The dog owner may even have some idea of what another dog, that he has never seen before, will do if he calls it. The effect uncertainty dimension is that the dynamics can be • deterministic – when the state resulting from an action is determined by an action and the prior state or • stochastic – when there is only a probability distribution over the resulting states.

In other situations, if the robot stays close to the center of the corridor, it may not need to model its width or the steering angles. Choosing an appropriate level of abstraction is difficult because • a high-level description is easier for a human to specify and understand. • a low-level description can be more accurate and more predictive. Often high-level descriptions abstract away details that may be important for actually solving the problem. • the lower the level, the more difficult it is to reason with.

An agent could be a program that acts in a purely computational environment – a software agent. 3 shows the inputs and outputs of an agent. 4. 3: An agent interacting with an environment • observations of the current environment and • past experiences of previous actions and observations, or other data, from which it can learn; • goals that it must try to achieve or preferences over states of the world; and • abilities, which are the primitive actions it is capable of carrying out. Two deterministic agents with the same prior knowledge, history, abilities, and goals should do the same thing.

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