Topics and features: provides application-focused and hands-on approach to learning the subject; provide training to learn the various levels of difficulty at the end of each chapter, with solutions given at the end of this book, supporting text is highlighted with examples, definitions, and theorems; including chapter on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for further reading on more advanced topics; supply of additional teaching resources, including lecture slides and data training for learning algorithms, on the relevant website.