Artificial Intelligence: A Systems Approach: A Systems ApproachThis book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This “sensor / algorithm / effecter” approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book. |
From inside the book
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... algorithms , code , or computer programs ( " the software " ) or any of the third party software contained on the CD - ROM or any of the textual material in the book , cannot and do not warrant the performance or results that might be ...
... algorithms , code , or computer programs ( " the software " ) or any of the third party software contained on the CD - ROM or any of the textual material in the book , cannot and do not warrant the performance or results that might be ...
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A Systems Approach M. Tim Jones. Machine Learning Evolutionary Computation Neural Networks Part 1 Neural Networks Part 2 ... Algorithm Advantages Chapter Summary 31 34 36 39 42 42 45 46 46 Algorithms Summary 46 References 47 Exercises 47 ...
A Systems Approach M. Tim Jones. Machine Learning Evolutionary Computation Neural Networks Part 1 Neural Networks Part 2 ... Algorithm Advantages Chapter Summary 31 34 36 39 42 42 45 46 46 Algorithms Summary 46 References 47 Exercises 47 ...
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... Algorithms Summary 52 56 57 59 59 61 64 65 65 65 66 68 68 70 73 75 77 79 80 81 81 83 84 84 84 84 86 86 86 86 References Resources Exercises 8887 Chapter 4 AI and Games 89-142 Two Player Games 89 The Minimax Algorithm 92 Minimax and Tic ...
... Algorithms Summary 52 56 57 59 59 61 64 65 65 65 66 68 68 70 73 75 77 79 80 81 81 83 84 84 84 84 86 86 86 86 References Resources Exercises 8887 Chapter 4 AI and Games 89-142 Two Player Games 89 The Minimax Algorithm 92 Minimax and Tic ...
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... learning Poker Player 119 Scrabble 120 Video Game AI 121 Applications of AI Algorithms in Video Games 122 Movement and Pathfinding 123 Table Lookup with Offensive and Defensive Strategy 123 NPC Behavior 129 Static State Machines 130 ...
... learning Poker Player 119 Scrabble 120 Video Game AI 121 Applications of AI Algorithms in Video Games 122 Movement and Pathfinding 123 Table Lookup with Offensive and Defensive Strategy 123 NPC Behavior 129 Static State Machines 130 ...
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... System Ontology Communication of Knowledge Common Sense Summary References Resources 165 Automatic Mathematician 166 167 167 168 169 169 169 Exercises Chapter 6 Machine Learning Machine Learning Algorithms Supervised Learning.
... System Ontology Communication of Knowledge Common Sense Summary References Resources 165 Automatic Mathematician 166 167 167 168 169 169 169 Exercises Chapter 6 Machine Learning Machine Learning Algorithms Supervised Learning.
Contents
LEASTMEANSQUARE LMS LEARNING | 255 |
LMS Implementation | 256 |
LEARNING WITH BACKPROPAGATION | 258 |
Backpropagation Algorithm | 260 |
Backpropagation Implementation | 261 |
Tuning Backpropagation | 267 |
PROBABILISTIC NEURAL NETWORKS PNN | 268 |
PNN Algorithm | 269 |
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Bidirectional Search | 35 |
IMPROVEMENTS | 38 |
ALGORITHM ADVANTAGES | 39 |
REFERENCES | 40 |
INFORMED SEARCH | 42 |
BESTFIRST SEARCH BESTFS | 43 |
BestFirst Search Implementation | 45 |
Variants of BestFirst Search | 49 |
A SEARCH | 50 |
A Search and the Eight Puzzle | 52 |
Search Implementation | 54 |
Eight Puzzle Demonstration with A | 57 |
A Variants | 58 |
SIMULATED ANNEALING SA | 59 |
The Traveling Salesman Problem TSP | 61 |
Simulated Annealing Implementation | 63 |
Simulated Annealing Demonstration | 66 |
TABU SEARCH | 68 |
Tabu Search Implementation | 70 |
Tabu Search Demonstration | 72 |
Tabu Search Variants | 73 |
CONSTRAINT SATISFACTION PROBLEMS CSP | 74 |
Scheduling as a CSP | 76 |
CONSTRAINTSATISFACTION ALGORITHMS | 77 |
MinConflicts Search | 79 |
RESOURCES | 80 |
AI AND GAMES | 82 |
THE MINIMAX ALGORITHM | 85 |
Minimax and TicTacToe | 88 |
Minimax Implementation for TicTacToe | 91 |
Minimax with AlphaBeta Pruning | 94 |
CLASSICAL GAME AI | 99 |
CheckerBoard Representation | 100 |
Opening Books | 101 |
Endgame Database | 102 |
ChessBoard Representation | 103 |
Minimax Search with AlphaBeta Pruning | 104 |
Othello | 105 |
Search Algorithm | 106 |
Go | 107 |
Opening Moves | 108 |
Endgame | 109 |
TDGammon | 110 |
Poker | 111 |
Loki A Learning Poker Player | 112 |
Scrabble | 113 |
VIDEO GAME AI | 114 |
Applications of AI Algorithms in Video Games | 115 |
Movement and Pathfinding | 116 |
NPC Behavior | 122 |
Static State Machines | 123 |
Layered Behavior Architectures | 124 |
Other ActionSelection Mechanisms | 125 |
Coals and Plans | 127 |
RealTime Strategy AI | 129 |
CHAPTER SUMMARY | 132 |
RESOURCES | 133 |
EXERCISES | 134 |
KNOWLEDGE REPRESENTATION | 136 |
TYPES OF KNOWLEDGE | 137 |
SEMANTIC NETWORKS | 138 |
FRAMES | 139 |
PROPOSITIONAL LOGIC | 142 |
Deductive Reasoning with Propositional Logic | 144 |
Limitations of Prepositional Logic | 145 |
Atomic Sentences | 146 |
Compound Sentences | 147 |
Quantifiers | 148 |
Information Retrieval and KR | 150 |
Representing and Reasoning about an Environment | 152 |
SEMANTIC WEB | 156 |
COMPUTATIONAL KNOWLEDGE DISCOVERY | 158 |
Automatic Mathematician AM | 159 |
ONTOLOGY | 160 |
COMMON SENSE | 161 |
CHAPTER SUMMARY | 162 |
EXERCISES | 163 |
MACHINE LEARNING | 164 |
Supervised Learning | 165 |
Creating a Decision Tree | 167 |
Characteristics of DecisionTree Learning | 169 |
Markov Models | 170 |
Word Generation with Markov Chains | 172 |
Markov Chain Implementation | 173 |
Other Applications of Markov Chains | 177 |
Nearest Neighbor Classification | 178 |
1NN Example | 179 |
kNN Example | 181 |
CHAPTER SUMMARY | 185 |
EVOLUTIONARY COMPUTATION | 188 |
Evolutionary Strategies | 189 |
Evolutionary Programming | 190 |
Genetic Programming | 191 |
BIOLOGICAL MOTIVATION | 192 |
GENETIC ALGORITHMS GA | 193 |
Genetic Algorithm Implementation | 197 |
GENETIC PROGRAMMING GP | 205 |
Genetic Programming Implementation | 208 |
EVOLUTIONARY STRATEGIES ES | 213 |
Evolutionary Strategies Algorithm | 214 |
Evolutionary Strategies Implementation | 216 |
DIFFERENTIAL EVOLUTION DE | 220 |
Differential Evolution Algorithm | 221 |
Differential Evolution Implementation | 223 |
PARTICLE SWARM OPTIMIZATION PSO | 229 |
Particle Swarm Implementation | 231 |
EVOLVABLE HARDWARE | 237 |
REFERENCES | 238 |
NEURAL NETWORKS I | 242 |
BIOLOGICAL MOTIVATION | 243 |
FUNDAMENTALS OF NEURAL NETWORKS | 244 |
Single Layer Perceptrons SLPs | 245 |
MultiLayer Perceptrons MLPs | 247 |
Supervised vs Unsupervised Learning Algorithms | 250 |
Perceptron Learning Algorithm | 252 |
Perceptron Implementation | 253 |
PNN Implementation | 270 |
OTHER NEURAL NETWORK ARCHITECTURES | 274 |
Recurrent Neural Network | 276 |
Defining the Outputs | 277 |
Number of Hidden Layers | 278 |
NEURAL NETWORKS II | 282 |
HEBBIAN LEARNING | 283 |
Hebbs Rule | 284 |
Hebb Rule Implementation | 285 |
SIMPLE COMPETITIVE LEARNING | 289 |
Vector Quantization | 290 |
Vector Quantization Implementation | 291 |
KMEANS CLUSTERING | 297 |
kMeans Algorithm | 298 |
kMeans Implementation | 300 |
ADAPTIVE RESONANCE THEORY ART | 306 |
ART1 Algorithm | 307 |
ARTImplementation | 309 |
HOPFIELD AUTOASSOCIATIVE MODEL | 315 |
Hopfield AutoAssociator Algorithm | 316 |
Hopfield Implementation | 317 |
CHAPTER SUMMARY | 320 |
REFERENCES | 321 |
ROBOTICS AND AI | 322 |
What is a Robot? | 323 |
A Sampling from the Spectrum of Robotics | 324 |
Taxonomy of Robotics | 325 |
Fixed | 326 |
Other Types of Robots | 327 |
NATURAL SENSING AND CONTROL | 329 |
PERCEPTION WITH SENSORS | 330 |
ACTUATION WITH EFFECTORS | 331 |
SIMPLE CONTROL ARCHITECTURES | 332 |
Reactive Control | 333 |
Other Control Systems | 335 |
Cell Decomposition | 336 |
Potential Fields | 337 |
GROUP OR DISTRIBUTED ROBOTICS | 338 |
ROBOT PROGRAMMING LANGUAGES | 339 |
RESOURCES | 340 |
INTELLIGENT AGENTS | 342 |
ANATOMY OF AN AGENT | 343 |
AGENT PROPERTIES AND AI | 344 |
Rationale | 345 |
Cooperative | 346 |
AGENT TAXONOMIES | 349 |
Virtual Character Agents | 350 |
Entertainment Agents | 351 |
ChatterBots | 353 |
Mobile Agents | 355 |
User Assistance Agent | 357 |
Information Gathering and Filtering | 358 |
Hybrid Agents | 359 |
Types of Architectures | 360 |
Deliberative Architectures | 361 |
Blackboard Architectures | 362 |
BeliefDesireIntention BDI Architecture | 363 |
Hybrid Architectures | 364 |
Architecture Descriptions | 365 |
Behavior Networks Reactive Architecture | 366 |
ATLANTIS Deliberative Architecture | 368 |
Homer Deliberative Arch | 369 |
BB1 Blackboard | 370 |
Procedural Reasoning System BDI | 371 |
Aglets Mobile | 372 |
Messengers Mobile | 373 |
Soar Hybrid | 375 |
Aglets | 376 |
Obliq | 377 |
Traditional Languages | 378 |
ACL FIPA Agent Communication Language | 381 |
CHAPTER SUMMARY | 382 |
REFERENCES | 383 |
EXERCISES | 384 |
BIOLOGICALLY INSPIRED AND HYBRID MODELS | 386 |
OneDimensional CAs | 387 |
TwoDimensional CAs | 388 |
Conway Application | 389 |
Turing Completeness | 391 |
Touch points | 393 |
Orchestrating Autonomic Managers | 394 |
Autonomic Summary | 395 |
Echo | 396 |
The Bug or Agent | 397 |
Variations of Artificial Life | 401 |
FUZZY SYSTEMS | 403 |
Fuzzy Logic Mapping | 404 |
Fuzzy Logic Operators | 407 |
Fuzzy Control | 408 |
EVOLUTIONARY NEURAL NETWORKS | 409 |
Simulated Evolution Example | 412 |
ANT COLONY OPTIMIZATION AGO | 416 |
Path Selection | 418 |
Pheromone Evaporation | 419 |
AGO Parameters | 423 |
Synthesizing Emotion | 424 |
RESOURCES | 425 |
THE LANGUAGES OF AI | 426 |
Functional Programming | 427 |
Imperative Programming | 430 |
ObjectOriented Programming OOP | 431 |
Logic Programming | 434 |
LANGUAGES OF AI | 435 |
The LISP Language | 436 |
Overview of the LISP Language | 437 |
Predicates | 438 |
Programs as Data | 440 |
Functions in LISP | 441 |
LISP Summary | 444 |
History of Scheme | 445 |
Predicates | 446 |
List Processing | 447 |
Conditions | 448 |
Iteration and Maps | 449 |
Procedures in Scheme | 450 |
Scheme Summary | 453 |
Predicates | 454 |
Variables | 455 |
Conditions | 456 |
Iteration and Maps | 457 |
Pattern Matching | 458 |
POP11 Summary | 461 |
History of Prolog | 462 |
List Processing | 463 |
Facts Rules and Evaluation | 464 |
Arithmetic Expressions | 471 |
Prolog Summary | 473 |
CHAPTER SUMMARY | 474 |
RESOURCES | 475 |
ABOUT THE CDROM | 478 |
INDEX | 480 |
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Artificial Intelligence: A Systems Approach: A Systems Approach M. Tim Jones No preview available - 2008 |
Common terms and phrases
Aglets alpha-beta pruning applications autonomic managers backpropagation behavior best-first search board configuration called CD-ROM cell centroid Chapter Chess classification CLOSED list cluster complex created database define differential evolution Elise environment evaluate Board evolutionary strategies example explore fitness function fuzzy game tree genetic algorithm goal graph heuristic identify implementation initial input intelligent iteration k-Means knowledge KQML language learning algorithms LISP logic loop machine matrix minimax mobile agent move mutation neural network neuron node Note objects OPEN list operation optimization output particle swarm particle swarm optimization path pattern perceptron pheromone player predicate problem Prolog prototype feature vectors provides random representation represents result robot rule sample search algorithm selection sensors shown in Figure shown in Listing simple simulated simulated annealing solution space Tabu search uninformed search unsupervised learning variable weights