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
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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