Title:
Hands-on AI with Java : smart gaming, robotics, and more
Personal Author:
Publication Information:
New York : McGraw-Hill, 2004
ISBN:
9780071424967
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010064135 | QA76.73.J38 W574 2004 | Open Access Book | Book | Searching... |
Searching... | 30000010122062 | QA76.73.J38 W574 2004 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
Shows you how to give computer game characters, role-playing and decision-making skills. This book helps you: simulate evolution to find solutions to problems; develop machine-to-human text interactions for help desks, adventure games, computerized tutorials, and Web agents; and, guide machine tools for intelligent behavior.
Table of Contents
| Acknowledgments | p. xi |
| Chapter 1 Artificial intelligence | p. 1 |
| Introduction | p. 2 |
| Audience | p. 4 |
| Purpose | p. 4 |
| Examples | p. 4 |
| Categories of Al | p. 5 |
| Validation | p. 7 |
| Statistics | p. 7 |
| Validating Filtering and Predictive Systems | p. 8 |
| Code: Strip Chart Display | p. 10 |
| Validating Optimizations | p. 17 |
| Validating Classification | p. 22 |
| Code: Hinton Diagram Display | p. 28 |
| And Beyond | p. 32 |
| Chapter 2 Computing Framework | p. 33 |
| Architecture Overview | p. 34 |
| Software Agents | p. 40 |
| Distributed Computing | p. 44 |
| Networking | p. 44 |
| Code: Threads | p. 48 |
| Code: Pipes | p. 51 |
| Code: Sockets | p. 53 |
| Code: Class Loader | p. 56 |
| Remote Method Invocation | p. 59 |
| Communication Language | p. 60 |
| Chapter 3 Control Systems | p. 63 |
| Introduction: Reflexes | p. 64 |
| Filtering | p. 65 |
| Electronic RC Filters | p. 65 |
| Convolution Filters | p. 66 |
| Code: Convolution Filter | p. 68 |
| Proportional-Integral-Derivative Control | p. 70 |
| Code: Simplified Heater Process | p. 72 |
| Fuzzy-Logic | p. 75 |
| Crisp Boolean Logic | p. 75 |
| Basic Fuzzy | p. 78 |
| Code: Singleton Fuzzy Logic | p. 81 |
| Code: Fuzzy Heater Controller | p. 89 |
| Code: Fuzzy Logic | p. 94 |
| Neural Reflexes | p. 101 |
| Chapter 4 Scripted Behavior | p. 103 |
| Data-Driven Intelligence | p. 104 |
| Finite State Machines | p. 105 |
| Designing an FSM | p. 106 |
| Table Driven FSM | p. 108 |
| Code: Table-Driven FSM | p. 110 |
| Code: FSM Library | p. 113 |
| Code: Coin Box Revisited | p. 124 |
| Markov Models | p. 131 |
| Code: Markov Sentence Generation | p. 136 |
| Frame-Based Intelligence and Chatbots | p. 140 |
| Pattern Matching | p. 141 |
| Scripts | p. 143 |
| Frames and Agendas | p. 143 |
| Chapter 5 Discrete Searching | p. 145 |
| Introduction | p. 146 |
| Brute-Force Searching | p. 148 |
| Depth-First | p. 148 |
| Breadth-First | p. 149 |
| Cheapest-First | p. 150 |
| Bi-directional | p. 151 |
| A* Search | p. 151 |
| Code: A* Engine | p. 153 |
| Code: A* Path Finding | p. 156 |
| Code: A* Panel Nesting | p. 159 |
| Two-Player Games | p. 162 |
| Min-Max Search | p. 163 |
| Alpha-Beta Pruning | p. 166 |
| Code: Alpha-Beta Engine | p. 169 |
| Code: Amoeba Game | p. 171 |
| Chapter 6 Searching State Space | p. 175 |
| What is State Space? | p. 176 |
| Reinforcement Learning | p. 176 |
| Core Concepts: Temporal Difference Learning | p. 177 |
| SARSA | p. 180 |
| Off-Policy Q-Learning | p. 182 |
| Eligiblity Traces | p. 182 |
| Code: Continuous Control | p. 184 |
| Representing State Space | p. 188 |
| Map Decompositions | p. 188 |
| Partioning Continuous Space | p. 189 |
| Genetic Algorithms | p. 192 |
| Encoding the Problem | p. 193 |
| Breeding | p. 200 |
| Mutation | p. 203 |
| Evalutating Fitness | p. 205 |
| Selection | p. 207 |
| Chapter 7 Thinking Logically | p. 209 |
| Logic | p. 210 |
| Zero Order | p. 210 |
| First Order | p. 214 |
| Second Order | p. 217 |
| Using Logic | p. 218 |
| Inference | p. 218 |
| Proofs | p. 219 |
| Unification | p. 222 |
| Resolution | p. 223 |
| Normalizing Sentences | p. 226 |
| Proof by Resolution | p. 229 |
| Code: Unifier | p. 230 |
| Code: Backward Chaining | p. 237 |
| Code: Forward Chaining | p. 242 |
| Knowledge Representation | p. 245 |
| Chapter 8 Supervised Neural Networks | p. 249 |
| Simulated Intelligence | p. 250 |
| Neural Models | p. 251 |
| Biological Neurons | p. 251 |
| Code: Hodgkin-Huxley Neural Model | p. 253 |
| Pulsed Neuron Computing | p. 256 |
| Computational Neurons | p. 258 |
| Perceptron | p. 261 |
| Code: Perceptron | p. 262 |
| Multiple Categories | p. 264 |
| Multi-Layer Perceptron | p. 264 |
| State Space | p. 266 |
| Delta Rule | p. 266 |
| Backpropagation | p. 267 |
| Code: Backpropagation Character Recognition | p. 271 |
| Momentum | p. 279 |
| Normalization | p. 281 |
| Filtering | p. 282 |
| Scaling | p. 282 |
| Z-Axis Normalization | p. 283 |
| Binary Representations | p. 284 |
| Softmax | p. 285 |
| Associative Memory | p. 286 |
| Bidirectional Associative Memory | p. 286 |
| Code: Hopfield Network | p. 287 |
| Advanced Concepts | p. 289 |
| Time-Series | p. 289 |
| Fuzzy-Neural | p. 290 |
| Growing Networks | p. 291 |
| Chapter 9 Unsupervised Neural Networks | p. 293 |
| Neurons Revisited | p. 294 |
| Hebb | p. 294 |
| Pattern Matching | p. 296 |
| Self-Organizing Maps | p. 298 |
| Basic Operation | p. 299 |
| Code: RGB Map | p. 303 |
| Network Variations | p. 308 |
| Supervised | p. 308 |
| Biologically Plausibility | p. 309 |
| Growing Networks | p. 310 |
| Elastic Networks for the Traveling Salesman Problem | p. 313 |
| Code: Fast Elastic Net | p. 317 |
| Document Processing | p. 321 |
| Words | p. 321 |
| Documents | p. 321 |
| Hierarchy and Time | p. 322 |
| Multi-Modal SOM | p. 323 |
| Hierarchical SOM | p. 324 |
| Time-Series | p. 326 |
| References | p. 329 |
| Index | p. 333 |
