This book uses an intelligent agent as the unifying theme throughout i.e., the problem of AI is to describe and build agents that receive percepts from the environment and perform actions, and each such agent is implemented by a function that maps percepts to actions.
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.
Tackles AI's philosophical critics head-on.
Gives equal emphasis to theory and practiceconsiders the basic concepts and mathematical methods of AI, what can and cannot be done with today's technology, at what cost, and using what techniques.
Integrates state-of-the art AI techniques intointelligent agent designs, using examples and exercises to lead the student from simple, reactive agents to full knowledge-based agents with natural language capabilities. Includes over 75 algorithms, and a variety of simulated environments for testing agent designs.
Includes numerous written and programming exercises in each chapter.
a text primarily intended for use in an undergraduate course or course sequence. It shows how intelligent agents can be built using AI methods and explains how different agent designs are appropriate depending on the nature of the task and environment. It uses examples and exercises to lead students from simple reactive agents to advanced planning agents with natural language capabilities. Annotation c. Book News, Inc., Portland, OR (booknews.com)