Ebook: Artificial intelligence: a modern approach

27-4: What if AI does succeed? -- A: Mathematical background -- A-1: Complexity analysis and O() notation -- A-2: Vectors, matrices, and linear algebra -- A-3: Probability distributions -- B: Notes on languages and algorithms -- B-1: Defining languages with Backus-Naur form (BNF) -- B-2: Describing algorithms with pseudocode -- B-3: Online help -- Bibliography -- Index.;20-4: Summary, bibliographical and historical notes, exercises -- 21: Reinforcement learning -- 21-1: Introduction -- 21-2: Passive reinforcement learning -- 21-3: Active reinforcement learning -- 21-4: Generalization in reinforcement learning -- 21-5: Policy search -- 21-6: Applications of reinforcement learning -- 21-7: Summary, bibliographical and historical notes, exercises -- 6: Communicating, Perceiving, And Acting -- 22: Natural language processing -- 22-1: Language models -- 22-2: Text classification -- 22-3: Information retrieval -- 22-4: Information extraction -- 22-5: Summary, bibliographical and historical notes, exercises -- 23: Natural language for communication -- 23-1: Phrase structure grammars -- 23-2: Syntactic analysis (parsing) -- 23-3: Augmented grammars and semantic interpretation -- 23-4: Machine translation -- 23-5: Speech recognition -- 23-6: Summary, bibliographical and historical notes, exercises -- 24: Perception -- 24-1: Image formation.;11: Planning and acting in the real world -- 11-1: Time, schedules, and resources -- 11-2: Hierarchical planning -- 11-3: Planning and acting in nondeterministic domains -- 11-4: Multiagent planning -- 11-5: Summary, bibliographical and historical notes, exercises -- 12: Knowledge representation -- 12-1: Ontological engineering -- 12-2: Categories and objects -- 12-3: Events -- 12-4: Mental events and mental objects -- 12-5: Reasoning systems for categories -- 12-6: Reasoning with default information -- 12-7: Internet shopping world -- 12-8: Summary, bibliographical and historical notes, exercises -- 4: Uncertain Knowledge And Reasoning -- 13: Quantifying uncertainty -- 13-1: Acting under uncertainty -- 13-2: Basic probability notation -- 13-3: Inference using full joint distributions -- 13-4: Independence -- 13-5: Bayes' rule and its use -- 13-6: Wumpus world revisited -- 13-7: Summary, bibliographical and historical notes, exercises -- 14: Probabilistic reasoning.;7-5: Propositional theorem proving -- 7-6: Effective propositional model checking -- 7-7: Agents based on propositional logic -- 7-8: Summary, bibliographical and historical notes, exercises -- 8: First-order logic -- 8-1: Representation revisited -- 8-2: Syntax and semantics of first-order logic -- 8-3: Using first-order logic -- 8-4: Knowledge engineering in first-order logic -- 8-5: Summary, bibliographical and historical notes, exercises -- 9: Inference in first-order logic -- 9-1: Propositional vs first-order inference -- 9-2: Unification and lifting -- 9-3: Forward chaining -- 9-4: Backward chaining -- 9-5: Resolution -- 9-6: Summary, bibliographical and historical notes, exercises -- 10: Classical planning -- 10-1: Definition of classical planning -- 10-2: Algorithms for planning as state-space search -- 10-3: Planning graphs -- 10-4: Other classical planning approaches -- 10-5: Analysis of planning approaches -- 10-6: Summary, bibliographical and historical notes, exercises.;16-7: Decision-theoretic expert systems -- 16-8: Summary, bibliographical and historical notes, exercises -- 17: Making complex decisions -- 17-1: Sequential decision problems -- 17-2: Value iteration -- 17-3: Policy iteration -- 17-4: Partially observable MDPs -- 17-5: Decisions with multiple agents: game theory -- 17-6: Mechanism design -- 17-7: Summary, bibliographical and historical notes, exercises.;4-4: Searching with partial observations -- 4-5: Online search agents and unknown environments -- 4-6: Summary, bibliographical and historical notes, exercises -- 5: Adversarial search -- 5-1: Games -- 5-2: Optimal decisions in games -- 5-3: Alpha-beta pruning -- 5-4: Imperfect real-time decisions -- 5-5: Stochastic games -- 5-6: Partially observable games -- 5-7: State-of-the-art game programs -- 5-8: Alternative approaches -- 5-9: Summary, bibliographical and historical notes, exercises -- 6: Constraint satisfaction problems -- 6-1: Defining constraint satisfaction problems -- 6-2: Constraint propagation: inference in CSPs -- 6-3: Backtracking search for CSPs -- 6-4: Local search for CSPs -- 6-5: Structure of problems -- 6-6: Summary, bibliographical and historical notes, exercises -- 3: Knowledge. Reasoning And Planning -- 7: Logical agents -- 7-1: Knowledge-based agents -- 7-2: Wumpus world -- 7-3: Logic -- 7-4: Propositional logic: a very simple logic.;24-2: Early image-processing operations -- 24-3: Object recognition by appearance -- 24-4: Reconstructing the 3D world -- 24-5: Object recognition form structural information -- 24-6: Using vision -- 24-7: Summary, bibliographical and historical notes, exercises -- 25: Robotics -- 25-1: Introduction -- 25-2: Robot hardware -- 25-3: Robotic perception -- 25-4: Planning to move -- 25-5: Planning uncertain movements -- 25-6: Moving -- 25-7: Robotic software architectures -- 25-8: Application domains -- 25-9: Summary, bibliographical and historical notes, exercises -- 7: Conclusions -- 26: Philosophical foundations -- 26-1: Weak AI: can machines act intelligently? -- 26-2: Strong AI: can machines really think? -- 26-3: Ethics and risks of developing artificial intelligence -- 26-4: Summary, bibliographical and historical notes, exercises -- 27: AI: Present and future -- 27-1: Agent components -- 27-2: Agent architectures -- 27-3: Are we going in the right direction?;1: Artificial Intelligence -- 1: Introduction -- 1-1: What is AI? -- 1-2: Foundations of artificial intelligence -- 1-3: History of artificial intelligence -- 1-4: State of the art -- 1-5: Summary, bibliographical and historical notes, exercises -- 2: Intelligent agents -- 2-1: Agents and environments -- 2-2: Good behavior: the concepts of rationality -- 2-3: Nature of environments -- 2-4: Structure of agents -- 2-5: Summary, bibliographical and historical notes, exercises -- 2: Problem-Solving -- 3: Solving problems by searching -- 3-1: Problem-solving agents -- 3-2: Example problems -- 3-3: Searching for solutions -- 3-4: Uninformed search strategies -- 3-5: Informed (heuristic) search strategies -- 3-6: Heuristic functions -- 3-7: Summary, bibliographical and historical notes, exercises -- 4: Beyond classical search -- 4-1: Local search algorithms and optimization problems -- 4-2: Local search in continuous spaces -- 4-3: Searching with nondeterministic actions.;14-1: Representing knowledge in an uncertain domain -- 14-2: Semantics of Bayesian networks -- 14-3: Efficient representation of conditional distributions -- 14-4: Exact inference in Bayesian networks -- 14-5: Approximate inference in Bayesian networks -- 14-6: Relational and first-order probability models -- 14-7: Other approaches to uncertain reasoning -- 14-8: Summary, bibliographical and historical notes, exercises -- 15: Probabilistic reasoning over time -- 15-1: Time and uncertainty -- 15-2: Inference in temporal models -- 15-3: Hidden Markov models -- 15-4: Kalman filters -- 15-5: Dynamic Bayesian Networks -- 15-6: Keeping track of many objects -- 15-7: Summary, bibliographical and historical notes, exercises -- 16: Making simple decisions -- 16-1: Combining beliefs and desires under uncertainty -- 16-2: Basis of utility theory -- 16-3: Utility functions -- 16-4: Multiattribute utility functions -- 16-5: Decision networks -- 16-6: Value of information.;Learning -- 18: Learning from examples -- 18-1: Forms of learning -- 18-2: Supervised learning -- 18-3: Learning decision trees -- 18-4: Evaluating and choosing the best hypothesis -- 18-5: Theory of learning -- 18-6: Regression and classification with linear models -- 18-7: Artificial neural networks -- 18-8: Nonparametric models -- 18-9: Support vector machines -- 18-10: Ensemble learning -- 18-11: Practical machine learning -- 18-12: Summary, bibliographical and historical notes, exercises -- 19: Knowledge in learning -- 19-1: Logical formulation of learning -- 19-2: Knowledge in learning -- 19-3: Explanation-based learning -- 19-4: Learning using relevance information -- 19-5: Inductive logic programming -- 19-6: Summary, bibliographical and historical notes, exercises -- 20: Learning probabilistic models -- 20-1: Statistical learning -- 20-2: Learning with complete data -- 20-3: Learning with hidden variables: the EM algorithm.