内容简介 · · · · · ·
This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.
Author(s): Ertel, Wolfgang
Publisher: Springer
Year: 2011
Language: English
Pages: 328
Cover
Undergraduate Topics in Computer Science
Introduction to Artificial Intelligence
ISBN 9780857292988
Preface
Contents
Chapter 1: Introduction
1.1 What Is Artificial Intelligence?
1.1.1 Brain Science and Problem Solving
1.1.2 The Turing Test and Chatterbots
1.2 The History of AI
1.2.1 The First Beginnings
1.2.2 Logic Solves (Almost) All Problems
1.2.3 The New Connectionism
1.2.4 Reasoning Under Uncertainty
1.2.5 Distributed, Autonomous and Learning Agents
1.2.6 AI Grows up
1.3 Agents
1.4 Knowledge-Based Systems
1.5 Exercises
Chapter 2: Propositional Logic
2.1 Syntax
2.2 Semantics
2.3 Proof Systems
2.4 Resolution
2.5 Horn Clauses
2.6 Computability and Complexity
2.7 Applications and Limitations
2.8 Exercises
Chapter 3: First-order Predicate Logic
3.1 Syntax
3.2 Semantics
3.2.1 Equality
3.3 Quantifiers and Normal Forms
3.4 Proof Calculi
3.5 Resolution
3.5.1 Resolution Strategies
3.5.2 Equality
3.6 Automated Theorem Provers
3.7 Mathematical Examples
3.8 Applications
3.9 Summary
3.10 Exercises
Chapter 4: Limitations of Logic
4.1 The Search Space Problem
4.2 Decidability and Incompleteness
4.3 The Flying Penguin
4.4 Modeling Uncertainty
4.5 Exercises
Chapter 5: Logic Programming with PROLOG
5.1 PROLOG Systems and Implementations
5.2 Simple Examples
5.3 Execution Control and Procedural Elements
5.4 Lists
5.5 Self-modifying Programs
5.6 A Planning Example
5.7 Constraint Logic Programming
5.8 Summary
5.9 Exercises
Chapter 6: Search, Games and Problem Solving
6.1 Introduction
6.2 Uninformed Search
6.2.1 Breadth-First Search
Analysis
6.2.2 Depth-First Search
Analysis
6.2.3 Iterative Deepening
Analysis
6.2.4 Comparison
6.3 Heuristic Search
6.3.1 Greedy Search
6.3.2 A-Search
6.3.3 IDA-Search
6.3.4 Empirical Comparison of the Search Algorithms
6.3.5 Summary
6.4 Games with Opponents
6.4.1 Minimax Search
6.4.2 Alpha-Beta-Pruning
Complexity
6.4.3 Non-deterministic Games
6.5 Heuristic Evaluation Functions
6.5.1 Learning of Heuristics
6.6 State of the Art
6.7 Exercises
Chapter 7: Reasoning with Uncertainty
7.1 Computing with Probabilities
7.1.1 Conditional Probability
Chain Rule
Marginalization
Bayes' Theorem
7.2 The Principle of Maximum Entropy
7.2.1 An Inference Rule for Probabilities
7.2.2 Maximum Entropy Without Explicit Constraints
7.2.3 Conditional Probability Versus Material Implication
7.2.4 MaxEnt-Systems
7.2.5 The Tweety Example
7.3 Lexmed, an Expert System for Diagnosing Appendicitis
7.3.1 Appendicitis Diagnosis with Formal Methods
7.3.2 Hybrid Probabilistic Knowledge Base
7.3.3 Application of Lexmed
7.3.4 Function of Lexmed
Learning of Rules by Statistical Induction
Determining the Dependency Graph
Estimating the Rule Probabilities
Expert Rules
Diagnosis Queries
7.3.5 Risk Management Using the Cost Matrix
Cost Matrix in the Binary Case
7.3.6 Performance
7.3.7 Application Areas and Experiences
7.4 Reasoning with Bayesian Networks
7.4.1 Independent Variables
7.4.2 Graphical Representation of Knowledge as a Bayesian Network
7.4.3 Conditional Independence
7.4.4 Practical Application
7.4.5 Software for Bayesian Networks
7.4.6 Development of Bayesian Networks
Lexmed as a Bayesian Network
Causality and Network Structure
7.4.7 Semantics of Bayesian Networks
7.5 Summary
7.6 Exercises
Chapter 8: Machine Learning and Data Mining
What Is Learning?
The Learning Agent
What Is Data Mining?
8.1 Data Analysis
8.2 The Perceptron, a Linear Classifier
8.2.1 The Learning Rule
8.2.2 Optimization and Outlook
8.3 The Nearest Neighbor Method
8.3.1 Two Classes, Many Classes, Approximation
8.3.2 Distance Is Relevant
8.3.3 Computation Times
8.3.4 Summary and Outlook
8.3.5 Case-Based Reasoning
8.4 Decision Tree Learning
8.4.1 A Simple Example
8.4.2 Entropy as a Metric for Information Content
8.4.3 Information Gain
8.4.4 Application of C4.5
8.4.5 Learning of Appendicitis Diagnosis
8.4.6 Continuous Attributes
8.4.7 Pruning-Cutting the Tree
8.4.8 Missing Values
8.4.9 Summary
8.5 Learning of Bayesian Networks
8.5.1 Learning the Network Structure
8.6 The Naive Bayes Classifier
Estimation of Probabilities
8.6.1 Text Classification with Naive Bayes
8.7 Clustering
8.7.1 Distance Metrics
8.7.2 k-Means and the EM Algorithm
8.7.3 Hierarchical Clustering
8.8 Data Mining in Practice
8.8.1 The Data Mining Tool KNIME
8.9 Summary
8.10 Exercises
8.10.1 Introduction
8.10.2 The Perceptron
8.10.3 Nearest Neighbor Method
8.10.4 Decision Trees
8.10.5 Learning of Bayesian Networks
8.10.6 Clustering
8.10.7 Data Mining
Chapter 9: Neural Networks
9.1 From Biology to Simulation
9.1.1 The Mathematical Model
9.2 Hopfield Networks
9.2.1 Application to a Pattern Recognition Example
9.2.2 Analysis
9.2.3 Summary and Outlook
9.3 Neural Associative Memory
9.3.1 Correlation Matrix Memory
9.3.2 The Pseudoinverse
9.3.3 The Binary Hebb Rule
9.3.4 A Spelling Correction Program
9.4 Linear Networks with Minimal Errors
9.4.1 Least Squares Method
9.4.2 Application to the Appendicitis Data
9.4.3 The Delta Rule
9.4.4 Comparison to the Perceptron
9.5 The Backpropagation Algorithm
9.5.1 NETtalk: A Network Learns to Speak
9.5.2 Learning of Heuristics for Theorem Provers
9.5.3 Problems and Improvements
9.6 Support Vector Machines
9.7 Applications
9.8 Summary and Outlook
9.9 Exercises
9.9.1 From Biology to Simulation
9.9.2 Hopfield Networks
9.9.3 Linear Networks with Minimal Errors
9.9.4 Backpropagation
9.9.5 Support Vector Machines
Chapter 10: Reinforcement Learning
10.1 Introduction
10.2 The Task
10.3 Uninformed Combinatorial Search
10.4 Value Iteration and Dynamic Programming
10.5 A Learning Walking Robot and Its Simulation
10.6 Q-Learning
10.6.1 Q-Learning in a Nondeterministic Environment
10.7 Exploration and Exploitation
10.8 Approximation, Generalization and Convergence
10.9 Applications
10.10 Curse of Dimensionality
10.11 Summary and Outlook
10.12 Exercises
Chapter 11: Solutions for the Exercises
11.1 Introduction
11.2 Propositional Logic
11.3 First-Order Predicate Logic
11.4 Limitations of Logic
11.5 PROLOG
11.6 Search, Games and Problem Solving
11.7 Reasoning with Uncertainty
11.8 Machine Learning and Data Mining
11.9 Neural Networks
11.10 Reinforcement Learning
References
Index
