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A thorough introduction to artificial intelligence (AI), this text provides a comprehensive and in-depth overview of the key concepts and techniques in AI. It also discusses the various areas and successes of the rational and emergent software approaches to AI. Throughout the text, theory is introduced via examples, giving readers the foundation to solve problems with logic that they can use later in AI-related research and other domains. The author also covers algorithms in sufficient detail, allowing readers to readily understand and implement them.
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Table of Contents

Introduction to Artificial IntelligenceHistory of Artificial IntelligenceContemporary Artificial Intelligence LOGICAL INTELLIGENCEPropositional LogicBasics of Propositional LogicResolutionArtificial Intelligence ApplicationsDiscussion and Further Reading First-Order LogicBasics of First-Order LogicArtificial Intelligence ApplicationsDiscussion and Further Reading Certain Knowledge RepresentationTaxonomic KnowledgeFramesNonmonotonic LogicDiscussion and Further Reading PROBABILISTIC INTELLIGENCEProbabilityProbability BasicsRandom VariablesMeaning of ProbabilityRandom Variables in Applications Probability in the Wumpus World Uncertain Knowledge RepresentationIntuitive Introduction to Bayesian NetworksProperties of Bayesian NetworksCausal Networks as Bayesian NetworksInference in Bayesian NetworksNetworks with Continuous VariablesObtaining the ProbabilitiesLarge-Scale Application: Promedas Advanced Properties of Bayesian Network Entailed Conditional Independencies Faithfulness Markov Equivalence Markov Blankets and Boundaries Decision Analysis Decision Trees Influence Diagrams Modeling Risk Preferences Analyzing Risk Directly Good Decision versus Good Outcome Sensitivity Analysis Value of InformationDiscussion and Further Reading EMERGENT INTELLIGENCEEvolutionary ComputationGenetics Review Genetic AlgorithmsGenetic ProgrammingDiscussion and Further Reading Swarm Intelligence Ant System Flocks Discussion and Further Reading LEARNINGLearning Deterministic Models Supervised Learning RegressionLearning a Decision Tree Learning Probabilistic Model Parameters Learning a Single Parameter Learning Parameters in a Bayesian Network Learning Parameters with Missing Data Learning Probabilistic Model Structure Structure Learning Problem Score-Based Structure LearningConstraint-Based Structure LearningApplication: MENTORSoftware Packages for Learning Causal LearningClass Probability TreesDiscussion and Further Reading More Learning Unsupervised Learning Reinforcement Learning Discussion and Further Reading LANGUAGE UNDERSTANDINGNatural Language Understanding ParsingSemantic Interpretation Concept/Knowledge Interpretation Information Extraction Discussion and Further Reading Bibliography Index

About the Author

Richard E. Neapolitan is a professor of computer science at Northeastern Illinois University. Dr. Neapolitan has published in prestigious journals related to reasoning under uncertainty and has authored five books: Probabilistic Reasoning in Expert Systems, Learning Bayesian Networks, Foundations of Algorithms, Probabilistic Methods for Financial and Marketing Informatics, and Probabilistic Methods for Bioinformatics. His research interests include probability and statistics, expert systems, cognitive science, and applications of probabilistic modeling to fields such as medicine, biology, and finance. Xia Jiang is an assistant professor in the Department of Biomedical Informatics at the University of Pittsburgh. Dr. Jiang co-authored Probabilistic Methods for Financial and Marketing Informatics. Her current research focuses on developing novel algorithms/systems that improve the computational efficiency of large data analysis and network modeling of cancer genome data.

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