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
Results 1-5 of 38
Page
... 11 460 Overview of the POP - 11 Language 460 Data Representation 460 Predicates 461 Simple Expressions 461 Variables List Processing Conditions 462 462 463 Iteration and Maps 464 Pattern Matching 465 Procedures in POP.
... 11 460 Overview of the POP - 11 Language 460 Data Representation 460 Predicates 461 Simple Expressions 461 Variables List Processing Conditions 462 462 463 Iteration and Maps 464 Pattern Matching 465 Procedures in POP.
Page
A Systems Approach M. Tim Jones. Iteration and Maps 464 Pattern Matching 465 Procedures in POP - 11 465 POP - 11 Summary 468 Prolog 468 History of Prolog 469 Overview of the Prolog Language 469 Data Representation 469 List Processing 470 ...
A Systems Approach M. Tim Jones. Iteration and Maps 464 Pattern Matching 465 Procedures in POP - 11 465 POP - 11 Summary 468 Prolog 468 History of Prolog 469 Overview of the Prolog Language 469 Data Representation 469 List Processing 470 ...
Page 6
... The design of Eliza would be considered simple by today's standards , but its pattern - matching abilities , which provided reasonable responses to 6 Artificial Intelligence Building Tools for AI The Focus on Strong AI.
... The design of Eliza would be considered simple by today's standards , but its pattern - matching abilities , which provided reasonable responses to 6 Artificial Intelligence Building Tools for AI The Focus on Strong AI.
Page 7
A Systems Approach M. Tim Jones. pattern - matching abilities , which provided reasonable responses to patient statements was real to many people . This quality of the program was troubling to Weizenbaum who later became a critic of AI ...
A Systems Approach M. Tim Jones. pattern - matching abilities , which provided reasonable responses to patient statements was real to many people . This quality of the program was troubling to Weizenbaum who later became a critic of AI ...
Page 11
... pattern recognition , and data mining . A very novel application of AIS is in computational security . The human body reacts to the presence of infections through the release of antibodies which destroy those infectious substances ...
... pattern recognition , and data mining . A very novel application of AIS is in computational security . The human body reacts to the presence of infections through the release of antibodies which destroy those infectious substances ...
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 |
9 | |
10 | |
12 | |
13 | |
15 | |
16 | |
17 | |
18 | |
19 | |
21 | |
22 | |
23 | |
25 | |
27 | |
29 | |
30 | |
31 | |
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 |
Other editions - View all
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