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Contemporary Artificial Intelligence

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 Intelligence History of Artificial Intelligence Contemporary Artificial Intelligence LOGICAL INTELLIGENCE Propositional Logic Basics of Propositional Logic Resolution Artificial Intelligence Applications Discussion and Further Reading First-Order Logic Basics of First-Order Logic Artificial Intelligence Applications Discussion and Further Reading Certain Knowledge Representation Taxonomic Knowledge Frames Nonmonotonic Logic Discussion and Further Reading PROBABILISTIC INTELLIGENCE Probability Probability Basics Random Variables Meaning of Probability Random Variables in Applications Probability in the Wumpus World Uncertain Knowledge Representation Intuitive Introduction to Bayesian Networks Properties of Bayesian Networks Causal Networks as Bayesian Networks Inference in Bayesian Networks Networks with Continuous Variables Obtaining the Probabilities Large-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 Information Discussion and Further Reading EMERGENT INTELLIGENCE Evolutionary Computation Genetics Review Genetic Algorithms Genetic Programming Discussion and Further Reading Swarm Intelligence Ant System Flocks Discussion and Further Reading LEARNING Learning Deterministic Models Supervised Learning Regression Learning 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 Learning Constraint-Based Structure Learning Application: MENTOR Software Packages for Learning Causal Learning Class Probability Trees Discussion and Further Reading More Learning Unsupervised Learning Reinforcement Learning Discussion and Further Reading LANGUAGE UNDERSTANDING Natural Language Understanding Parsing Semantic 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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