Artifical Intellingence : Modern Approach 2/e

Artificial Intelligence 1. Introduction 2. Intelligent Agents II Problem-Solving 3. Solving Problems by Searching 4. Informed Search and Exploration 5. Constraint Satisfaction Problems 6. Adversarial Search III Knowledge and Reasoning 7. Logical Agents 8. First-Order Logic 9. Inference in First-Order Logic 10. Knowledge Representation IV Planning 11. Planning 12. Planning and Acting in the Real World V Uncertain Knowledge and Reasoning 13. Uncertainty 14. Probabilistic Reasoning 15. Probabilistic Reasoning over Time 17. Making Complex Decisions VI Learning 18. Learning from Observations 19. Knowledge in Learning 20. Statistical Learning Methods 21. Reinforcement Learning VII Communicating, Perceiving, and Acting 22. Communication 23. Probabilistic Language Processing 24. Perception 25. Robotics VIII Conclusions 26. Philosophical Foundations 27. AI : Present and Future Appendix Bibliography Index This classic text provides a thorough analysis of the field of Artificial Intelligence (AI) and gives a clear exposition of its funda-mental principles and applications. It covers such important topics as logic, probability, learning, microelectronic devices and robotic planetary explorers. The unifying theme that permeates the entire text is that of an intelligent agent, i.e. a system that can decide what to do and then do it. Divided into eight parts, the text begins with an introduction to the subject (AI) based on the idea of intelligent agents. Then follows a detailed discussion on problem solving, first-order logic, the methods for deciding ways to represent knowledge, planning, probabilistic reasoning, and decision mak-ing under uncertain conditions. Besides, the text explores the multifaceted aspects and various ways of learning, and delves deep into the interesting areas of communication and perception as well as the contemporary field of robotics. The book concludes with a broad survey of the current and future scenarios of Artificial Intelligence. A distinguishing feature of the book is that it covers significant areas like constraint satisfaction, fast propositional inference, planning graphs, internet agents, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, and ethical aspects of AI. The book, with its contemporary approach and extensive coverage, would be invaluable to the students of computer science and professionals in the field.