Integrates state-of-the-art AI techniques into intelligent agent designs, using examples and exercises to lead the reader from simple reactive agents to full knowledge-based agents with natural language capabilities. Covers areas that are sometimes under-emphasized--reasoning under uncertainty, learning, natural language, vision and robotics--and explains in detail some of the more recent ideas in the field e.g., simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural nets, inductive logic, programming, computational learning theory, and reinforcement learning. This highly accessible and well-written, state-of-the-art professional reference is for programmers, software engineers, system administrators, or technical managers who want to learn about or use A.I. techniques and software solutions.
The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.
Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning.