کتاب مقدمه‌ای بر هوش مصنوعی (۲۰۲۵)

Contents
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
1.2.7 The Deep Learning Breakthrough
1.2.8 The AI Revolution
1.3 AI and Society
1.3.1 Does AI Destroy Jobs?
1.3.2 AI and Transportation
1.3.3 Service Robotics
1.4 Agents
1.5 Knowledge-Based Systems
1.6 Exercises
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
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
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
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
6 Search, Games and Problem Solving
6.1 Introduction
6.2 Uninformed Search
6.2.1 Breadth-First Search
6.2.2 Depth-First Search
6.2.3 Iterative Deepening
6.2.4 Comparison
6.2.5 Cycle Check
6.3 Heuristic Search
6.3.1 Greedy Search
6.3.2 A-Search
6.3.3 Route Planning with the A Search Algorithm
6.3.4 IDA-Search
6.3.5 Empirical Comparison of the Search Algorithms
6.3.6 Summary
6.4 Games with Opponents
6.4.1 Minimax Search
6.4.2 Alpha-Beta-Pruning
6.4.3 Nondeterministic Games
6.5 Heuristic Evaluation Functions
6.5.1 Learning of Heuristics
6.6 State of the Art
6.6.1 Chess
6.6.2 Go
6.7 Exercises
7 Reasoning with Uncertainty
7.1 Computing with Probabilities
7.1.1 Conditional Probability
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
7.3.5 Risk Management Using the Cost Matrix
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
7.4.7 Semantics of Bayesian Networks
7.5 Summary
7.6 Exercises
8 Machine Learning and 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 Nearest Neighbor Methods
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 Data Normalization
8.5 Quality Metrics for Classifiers
8.6 Decision Tree Learning
8.6.1 A Simple Example
8.6.2 Entropy as a Metric for Information Content
8.6.3 Information Gain
8.6.4 Application of C4.5
8.6.5 Learning of Appendicitis Diagnosis
8.6.6 Continuous Attributes
8.6.7 Pruning–Cutting the Tree
8.6.8 Missing Values
8.6.9 Summary
8.7 Cross-Validation and Overfitting
8.8 Data Augmentation
8.9 Learning of Bayesian Networks
8.9.1 Learning the Network Structure
8.10 The Naive Bayes Classifier
8.10.1 Text Classification with Naive Bayes
8.11 One-Class Learning
8.11.1 Nearest Neighbor Data Description
8.12 Clustering
8.12.1 Distance Metrics
8.12.2 K-Means and the EM Algorithm
8.12.3 Hierarchical Clustering
8.12.4 How is the Number of Clusters Determined?
8.13 Data Mining in Practice
8.13.1 The Data Mining Tool KNIME
8.14 Summary
8.14.1 Literature
8.15 Exercises
8.15.1 Introduction
8.15.2 The Perceptron
8.15.3 Nearest Neighbor Method
8.15.4 Data Normalization
8.15.5 Quality Metrics for Classifiers
8.15.6 Decision Trees
8.15.7 Learning of Bayesian Networks
8.15.8 Clustering
8.15.9 Data Mining
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 Binary Hebb Rule
9.3.3 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
9.4.5 Logistic Regression
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 Deep Learning
9.6.1 Nature as Example
9.6.2 Stacked Denoising Autoencoder
9.6.3 Convolutional Neural Networks
9.6.4 A Handful of Tricks Leads to Success
9.6.5 Object Recognition
9.6.6 Systems and Implementations
9.6.7 An Example Program
9.7 Creativity
9.7.1 Generative Adversarial Networks
9.8 Transformers Take Over Natural Language Processing
9.9 Support Vector Machines
9.10 Summary and Outlook
9.10.1 Deep Learning Will Change the World
9.11 Exercises
9.11.1 From Biology to Simulation
9.11.2 Hopfield Networks
9.11.3 Linear Networks with Minimal Errors
9.11.4 Backpropagation
9.11.5 Deep Learning
9.11.6 Support Vector Machines
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 AlphaGo, the Breakthrough in Go
10.10.1 AlphaGoZero
10.11 Curse of Dimensionality
10.12 Summary and Outlook
10.13 Exercises
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