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Table of Contents Chapter 1 Introduction to AI Introduction What is Intelligence? The Search for Mechanical Intelligence The Very Early Days (the early 1950's) Alan Turing AI, Problem Solving and Games Artificial Intelligence Emerges as a Field The Dartmouth AI Summer Research Project Building Tools for AI The Focus on Strong AI Constrained Applications Bottom-Up Approaches Emerge AI's Winter Results-Oriented Applications Additional AI Tools Emerge Neat vs. Scruffy Approaches AI Remerges The Silent Return Messy and Scruffy Approaches Take Hold Agent Systems AI Inter-disciplinary R&D Systems Approach Overview of this Book Uninformed Search Informed Search AI and Games Knowledge Representation Machine Learning Evolutionary Computation Neural Networks Part 1 Neural Networks Part 2 Intelligent Agents Biologically Inspired and Hybrid Models Languages of AI Summary References Resources Exercises Chapter 2 Uninformed Search Search and AI Classes of Search General State Space Search Search in a Physical Space Search in a Puzzle Space Search in an Adversarial Game Space Trees, Graphs and Representation Uninformed Search Helper APIs General Search Paradigms Depth-First Search Depth-Limited Search Iterative Deepening Search Breadth-First Search Bidirectional Search Uniform-Cost Search Improvements Algorithm Advantages Chapter Summary Algorithms Summary References Exercises Chapter 3 Informed Search Search and AI Best-First Search Best-First Search and the N-Queens Problem Best-First Search Implementation Variants of Best-First Search A* Search A* Search and the Eight Puzzle Eight Puzzle Representation A* Search Implementation Eight Puzzle Demonstration with A* A* Variants Applications of A* Search Hill Climbing Search Simulated Annealing The Traveling Salesman Problem (TSP) TSP Tour Representation Simulated Annealing Implementation Simulated Annealing Demonstration Tabu Search Tabu Search Implementation Tabu Search Demonstration Tabu Search Variants Constraint Satisfaction Graph Coloring as a CSP Scheduling as CSP Constraint Satisfaction Problems Generate and Test Backtracking Forward Checking and Look Ahead Min-Conflicts Search Chapter Summary Algorithms Summary References Resources Exercises Chapter 4 AI and Games Two Player Games The Minimax Algorithm Minimax and Tic-Tac-Toe Minimax Implementation for Tic-Tac-Toe Minimax with Alpha-Beta Pruning Classical Game AI Checkers Checker Board Representation Techniques used in Checkers Programs Opening Books Static Evaluation Function Search Algorithm Move History Endgame Database Chess Chess Board Representation Techniques used in Chess Programs Opening Book Database Minimax Search with Alpha Beta Pruning Static Board Evaluation Othello Techniques used in Othello Programs Opening Knowledge Static Evaluation Function Search Algorithm Endgames Other Algorithms Go Go Board Representation Techniques used in Go Programs Opening Moves Move Generation Evaluation Endgame Backgammon Techniques used in Backgammon Programs Neurogammon TD-Gammon Poker Loki - A learning Poker Player Scrabble Video Game AI Applications of AI Algorithms in Video Games Movement and Pathfinding Table Lookup with Offensive and Defensive Strategy NPC Behavior Static State Machines Layered Behavior Architectures Other Action-Selection Mechanisms Team AI Goals and Plans Real-Time Strategy AI Rules-Based Programming Chapter Summary References Resources Exercises Chapter 5 Knowledge Representation Introduction Types of Knowledge The Role of Knowledge Semantic Nets Frames Propositional Logic Deductive Reasoning with Propositional Logic Limitations of Propositional Logic First Order Logic (Predicate Logic) Atomic Sentences Compound Sentences Variables Quantifiers First-Order Logic and Prolog Simple Example Information Retrieval and KR Representing and Reasoning about an Environment Semantic Web Computational Knowledge Discovery The BACON System Automatic Mathematician Ontology Communication of Knowledge Common Sense Summary References Resources Exercises Chapter 6 Machine Learning Machine Learning Algorithms Supervised Learning Learning with Decision Trees Creating a Decision Tree Characteristics of Decision Tree Learning Unsupervised Learning Markov Models Word Learning with Markov Chains Word Generation with Markov Chains Markov Chain Implementation Other Applications of Markov Chains Nearest Neighbor Classification 1NN Example k-NN Example Summary Resources Exercises Chapter 7 Evolutionary Computation Short History of Evolutionary Computation Evolutionary Strategies Evolutionary Programming Genetic Algorithms Genetic Programming Biological Motivation Genetic Algorithms Genetic Algorithm Overview Genetic Algorithm Implementation Genetic Programming Genetic Programming Algorithm Genetic Programming Implementation Evolutionary Strategies Evolutionary Strategies Algorithm Evolutionary Strategies Implementation Differential Evolution Differential Evolution Algorithm Differential Evolution Implementation Particle Swarm Optimization Particle Swarm Algorithm Particle Swarm Implementation Evolvable Hardware Summary References Resources Exercises Chapter 8 Neural Networks I Short History of Neural Networks Biological Motiviation Fundamentals of Neural Networks Single Layer Perceptrons Multi-Layer Perceptrons Supervised vs. Unsupervised Learning Algorithms Binary vs. Continuous Inputs and Outputs The Perceptron Perceptron Learning Algorithm Perceptron Implementation Least-Mean-Square (LMS) Learning LMS Learning Algorithm LMS Implementation Learning with Backpropagation Backpropagation Algorithm Backpropagation Implementation Tuning Backpropagation Training Variants Weight Adjustment Variants Probabilistic Neural Networks PNN Algorithm PNN Implementation Other Neural Network Architectures Time Series Processing Architecture Recurrent Neural Network Tips for Building Neural Networks Defining the Inputs Defining the Outputs Choice of Activation Functions Number of Hidden Layers Chapter Summary References Exercises Chapter 9 Neural Networks II Unsupervised Learning Hebbian Learning Hebb's Rule Hebb Rule Implementation Simple Competitive Learning Vector Quantization Vector Quantization Implementation k-Means Clustering k-Means Algorithm k-Means Implementation Adaptive Resonance Theory ART-1 Algorithm ART-1 Implementation Hopfield Auto-Associative Model Hopfield Auto-Associator Algorithm Hopfield Implementation Summary References Exercises Chapter 10 Robotics and AI Introduction to Robotics What is a Robot? A Sampling from the Spectrum of Robotics Taxonomy of Robotics Fixed Legged Wheeled Underwater Aerial Other Types of Robots Hard vs. Soft Robotics Braitenburg Vehicles Natural Sensing and Control Perception with Sensors Actuation with Effectors Robotic Control Systems Simple Control Architectures Reactive Control Subsumption Other Control Systems Movement Planning Complexities of Motion Planning Cell Decomposition Potential Fields Group or Distributed Robotics Robot Programming Languages Robot Simulators Summary References Resources Exercises Chapter 11 Robotics and AI Anatomy of an Agent Agent Properties and AI Rationale Autonomous Persistent Communicative Cooperative Mobile Adaptive Agent Environments Agent Taxonomies Interface Agents Virtual Character Agents Entertainment Agents Game Agents ChatterBots Eliza and Parry AIML Mobile Agents User Assistance Agent Email Filtering Information Gathering and Filtering Other User-Assistance Applications Hybrid Agent Agent Architectures What is Architecture? Types of Architectures Reactive Architectures Deliberative Architectures Blackboard Architectures BDI Architecture Hybrid Architectures Mobile Architectures Architecture Description Subsumption Architecture (Reactive) Behavior Networks (Reactive) ATLANTIS (Deliberative) Homer (Deliberative) BB1 (Blackboard) Open Agent Architecture (Blackboard) Procedural Reasoning System (BDI) Aglets (Mobile) Messengers (Mobile) SOAR (Hybrid) Agent Languages Telescript Aglets Obliq Agent TCL Traditional Languages Agent Communication Knowledge Query and Manipulation Language (KQML) FIPA Agent Communication Language Extensible Markup Language (XML) Summary Resources References Exercises Chapter 12 Biologically Inspired and Hybrid Models Cellular Automata One Dimensional CA Two Dimensional CA Conway Application Turing Completeness Emergence and Organization Artificial Immune Systems Self-Management Capabilities Touchpoints Touchpoint Autonomic Managers Orchestrating Autonomic Managers Integrated Management Console Autonomic Summary Artificial Life Echo Tierra Simulated Evolution Environment The Bug (or Agent) Variations of Artificial Life Lindenmayer Systems Fuzzy Logic Introduction to Fuzzy Logic Fuzzy Logic Mapping Fuzzy Logic Operators Fuzzy Control Evolutionary Neural Networks Genetically Evolved Neural Networks Simulation Evolution Example Ant Colony Optimization Traveling Salesman Problem Path Selection Pheromone Intensification Pheromone Evaporation New Tour Sample Usage ACO Parameters Affective Computing Characterizing Human Emotion Synthesizing Emotion Resources Chapter 13 The Languages of AI Language Taxonomy Functional Programming Imperative Programming Object Oriented Programming Logic Programming Languages of AI The LISP Language The History of the LISP Language Overview of the LISP Language Data Representation Simple Expressions Predicates Variables List Processing Programs as Data Conditions Functions in LISP LISP Summary The Scheme Language History of Scheme Overview of the Scheme Language Data Representation Simple Expressions Predicates Variables List Processing Conditions Iteration and Maps Procedures in Scheme Scheme Summary The POP-11 Language History of POP-11 Overview of the POP-11 Language Data Representation Predicates Simple Expressions Variables List Processing Conditions Iteration and Maps Pattern Matching Procedures in POP-11 POP-11 Summary Prolog History of Prolog Overview of the Prolog Language Data Representation List Processing Facts, Rules, and Evaluation Arithmetic Expressions Prolog Summary Other Languages Chapter Summary References Resources Exercises
Library of Congress Subject Headings for this publication:
Artificial intelligence -- Data processing.
Artificial intelligence -- Mathematical models.