## Artificial Intelligence: Structures and Strategies for Complex Problem SolvingIn this accessible, comprehensive text, George Luger captures the essence of artificial intelligence-solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: "AI Algorithms in Prolog, Lisp and Java (TM). "References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence. |

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### Other editions - View all

Artificial Intelligence: Structures and Strategies for Complex Problem Solving George F. Luger No preview available - 2009 |

Artificial Intelligence: Structures and Strategies for Complex Problem Solving George F. Luger No preview available - 2011 |

### Common terms and phrases

8-puzzle agent applied approach arcs artificial intelligence backtrack Bayesian best-first search breadth-first search called Chapter clause complex components concept conceptual graphs consider D E FIN data-driven defined depth-first depth-first search described determine elements evaluation example expert systems Figure finite state machine formal function genetic genetic algorithms goal goal-driven heuristic search human implement important inference rules input instance interpretation knowledge base logic machine learning match memory modus ponens move natural language node operators output parse pattern possible predicate calculus expressions presented probabilistic probability problem domain problem solving production system Prolog propositional calculus reasoning recursive relationships represent representation result robot search algorithms search space Section semantic sentence sequence situation solver space search stochastic strategy strings structure subgoals substitutions symbols techniques theorem theory tic-tac-toe tion transition tree true truth value unification unify variable vector verb