Contemporary Artificial Intelligence

Contemporary Artificial Intelligence pdf epub mobi txt 电子书 下载 2025

出版者:Chapman and Hall/CRC
作者:Richard E. Neapolitan
出品人:
页数:515
译者:
出版时间:2012-8-25
价格:USD 104.95
装帧:Hardcover
isbn号码:9781439844694
丛书系列:
图书标签:
  • 美國
  • 概率論
  • 數學
  • 人工智能
  • CS
  • 人工智能
  • 机器学习
  • 深度学习
  • 自然语言处理
  • 计算机视觉
  • 强化学习
  • AI伦理
  • 知识表示
  • 专家系统
  • 智能系统
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具体描述

作者简介

目录信息

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
· · · · · · (收起)

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