This concise and accessible textbook supports a foundation
or module course on A.I., covering a broad selection of the
subdisciplines within this field. The book presents concrete algorithms
and applications in the areas of agents, logic, search, reasoning under
uncertainty, machine learning, neural networks and reinforcement
learning. Topics and features: presents an application-focused and
hands-on approach to learning the subject; provides study exercises of
varying degrees of difficulty at the end of each chapter, with solutions
given at the end of the book; supports the text with highlighted
examples, definitions, and theorems; includes chapters on predicate
logic, PROLOG, heuristic search, probabilistic reasoning, machine
learning and data mining, neural networks and reinforcement learning;
contains an extensive bibliography for deeper reading on further topics;
supplies additional teaching resources, including lecture slides and
training data for learning algorithms, at an associated website.
