Course (2-8-1) Artificial Intelligence Programming in Java
- 1 Introduction 1
- 1.1 Other JVM Languages
- 1.2 Why is a PDF Version of this Book Available Free on the Web?
- 1.3 Book Software
- 1.4 Use of Java Generics and Native Types
- 1.5 Notes on Java Coding Styles Used in this Book
- 1.6 Book Summary
- 2 Search 5
- 2.1 Representation of Search State Space and Search Operators
- 2.2 Finding Paths in Mazes
- 2.3 Finding Paths in Graphs
- 2.4 Adding Heuristics to Breadth First Search
- 2.5 Search and Game Playing
- 2.5.1 Alpha-Beta Search
- 2.5.2 A Java Framework for Search and Game Playing
- 2.5.3 Tic-Tac-Toe Using the Alpha-Beta Search Algorithm
- 2.5.4 Chess Using the Alpha-Beta Search Algorithm
- 3 Reasoning
- 3.1 Logic
- 3.1.1 History of Logic
- 3.1.2 Examples of Different Logic Types
- 3.2 PowerLoom Overview
- 3.3 Running PowerLoom Interactively
- 3.4 Using the PowerLoom APIs in Java Programs
- 3.5 Suggestions for Further Study
- 3.1 Logic
- 4 Semantic Web
- 4.1 Relational Database Model Has Problems Dealing with Rapidly Changing Data Requirements
- 4.2 RDF: The Universal Data Format
- 4.3 Extending RDF with RDF Schema
- 4.4 The SPARQL Query Language
- 4.5 Using Sesame
- 4.6 OWL: The Web Ontology Language
- 4.7 Knowledge Representation and REST
- 4.8 Material for Further Study
- 5 Expert Systems
- 5.1 Production Systems
- 5.2 The Drools Rules Language
- 5.3 Using Drools in Java Applications
- 5.4 Example Drools Expert System: Blocks World
- 5.4.1 POJO Object Models for Blocks World Example
- 5.4.2 Drools Rules for Blocks World Example
- 5.4.3 Java Code for Blocks World Example
- 5.5 Example Drools Expert System: Help Desk System
- 5.5.1 Object Models for an Example Help Desk
- 5.5.2 Drools Rules for an Example Help Desk
- 5.5.3 Java Code for an Example Help Desk
- 5.6 Notes on the Craft of Building Expert Systems
- 6 Genetic Algorithms
- 6.1 Theory
- 6.2 Java Library for Genetic Algorithms
- 6.3 Finding the Maximum Value of a Function
- 7 Neural Networks
- 7.1 Hopï¬eld Neural Networks
- 7.2 Java Classes for Hopï¬eld Neural Networks
- 7.3 Testing the Hopï¬eld Neural Network Class
- 7.4 Back Propagation Neural Networks
- 7.5 A Java Class Library for Back Propagation
- 7.6 Adding Momentum to Speed Up Back-Prop Training
- 8 Machine Learning with Weka
- 8.1 Using Weka’s Interactive GUI Application
- 8.2 Interactive Command Line Use of Weka
- 8.3 Embedding Weka in a Java Application
- 8.4 Suggestions for Further Study
- 9 Statistical Natural Language Processing
- 9.1 Tokenizing, Stemming, and Part of Speech Tagging Text
- 9.2 Named Entity Extraction From Text
- 9.3 Using the WordNet Linguistic Database
- 9.3.1 Tutorial on WordNet
- 9.3.2 Example Use of the JAWS WordNet Library
- 9.3.3 Suggested Project: Using a Part of Speech Tagger to Use the Correct WordNet Synonyms
- 9.3.4 Suggested Project: Using WordNet Synonyms to Improve Document Clustering
- 9.4 Automatically Assigning Tags to Text
- 9.5 Text Clustering
- 9.6 Spelling Correction
- 9.6.1 GNU ASpell Library and Jazzy
- 9.6.2 Peter Norvig’s Spelling Algorithm
- 9.6.3 Extending the Norvig Algorithm by Using Word Pair Statistics
- 9.7 Hidden Markov Models
- 9.7.1 Training Hidden Markov Models
- 9.7.2 Using the Trained Markov Model to Tag Text Information Gathering
- 10.1 Open Calais
- 10.2 Information Discovery in Relational Databases
- 10.2.1 Creating a Test Derby Database Using the CIA World FactBook and Data on US States
- 10.2.2 Using the JDBC Meta Data APIs
- 10.2.3 Using the Meta Data APIs to Discern Entity Relationships
- 10.3 Down to the Bare Metal: In-Memory Index and Search
- 10.4 Indexing and Search Using Embedded Lucene
- 10.5 Indexing and Search with Nutch Clients
- 10.5.1 Nutch Server Fast Start Setup
- 10.5.2 Using the Nutch OpenSearch Web APIs
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