Artificial Intelligence: A Modern ApproachPrentice Hall, 2010 - 1132 頁 Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times , the course on artificial intelligence is "one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world." One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle(TM), NOOK(TM), and the iPhone(R)/iPad(R). To learn more about the course on artificial intelligence, visit http: //www.ai-class.com. To read the full New York Times article, click here. |
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action admissible heuristic applied approach attributes axioms Bayes Bayesian networks belief Boolean branching factor breadth-first search called Chapter chess choose clauses complexity conditional independence consider consistent constraint cost decision tree defined depth-first search described distribution domain environment Equation error estimate evaluation example false first-order logic game tree Gaussian given goal graph heuristic inference initial input iteration Kalman filter knowledge base language learning algorithm linear literals Markov methods minimax move mutex n-gram node objects optimal outcome parameters partially observable path percept player predicate probabilistic probability problem propositional logic query random reasoning reinforcement learning represent representation robot rule samples SATPLAN search algorithms Section semantics sensor sentence sequence shown in Figure shows simple solution solve square step stochastic strategy structure Suppose symbols theorem theory transition model true uniform-cost search update utility function variables wumpus