69_M._Tim_Jones_Artificial_Intelligence._A_Systems_(b-ok.org)_2008_516

The Languages of AI 483

15. In Scheme, provide the evaluation for (list ‘(a b) ‘(c d) ’d).
16. In Scheme, what is the evaluation of (length ‘(()))?
17. Provide the result of this Scheme example:
(map (lambda (x) (+ x 1) ‘(1 2 3 4))
18. Create a Scheme function that emits the Fibonacci sequence for a

defined limit.
19. Write a Scheme function that emits the age for a name in the example

database.
20. How does POP-11 compare to LISP and also to imperative languages

like C?
21. Describe the differences in list processing between Scheme and POP-11.
22. Build a simple conversational program in POP-11 that supports simple

English language interactions.
23. Define how the Prolog language is different from other languages

discussed in this chapter.
24. Describe the fundamental list-processing operations provided by

Prolog.
25. Extend the Prolog family tree example for two new rules, grandson and

granddaughter.



AA p p e n d i x ABOUT
THE CD-ROM

• Included on the CD-ROM are simulations, code, videos, figures from
the text, third party software, and other files related to topics in artificial
intelligence.

• See the “README" files for any specific information/system
requirements related to each file folder, but most files will run on
Windows 2000 or higher and Linux.



INDEX

1NN, 185-192 Adaptive Resonance Theory (ART),
2001: A Space Odyssey, 2 257, 313-322
4-Queens problem, 76-80
a priori, 305-307 Adjacency lists, 29-30, 68-70
A Adjacency matrix, 28-29
A* search algorithm, 123 Affective computing, 430-432
A*Search, 57-63
Synthesizing emotion, 431-433
Eight Puzzle, 59-66 Agency, 12
Implementation, 61-64 Agent architectures, 366-382
Applications of, 65 Agent communication, 385-389
A* variants, 65 Agent environments, 353-356
ACL (FIPA Agent Communicative Agent languages, 382-385
Language), 388 Agent properties and AI, 352-353
ACO algorithm, 424 Agent systems, 12
ACO, 10, 423-430 Agent taxonomies, 356-366
Activation functions, 285 Agent TCL, 384-385
Active networking, 364 Agents, 351-353, 404-405
Active Node Transfer System (ANTS), Aglets, 363-364, 379-380, 383-384
364 AI and games, 15
Actuation with effectors, 338 AI effect, 10
Ada, 437 AI@50, 6
ADALINE, 250, 265 AIML, 361-362
AIS, 11
ALGOL, 437

488 Artificial Intelligence

ALife, 11 Automatic computing systems, 400
Alpha parameter, 62 Automatic Mathematician (AM), 165-
Alpha-beta pruning, 101-106
AM, 166-167 168
Ananova, 358 Avoidance behavior, 341
Anatomy of an agent, 350-351 Axons, 251
And-or graphs, 115 B
Annealing, 66-68 Backgammon, 116-117
Ant Colony Optimization (ACO), 10, Backpropagation, 250, 257, 265-270

199, 423-430 Algorithm, 250, 267-268
Traveling salesman problem, 423- Implementation, 268-270
424 Tuning, 274
ACO algorithm, 424 Training variants, 274
ACO parameters, 430 Weight adjustment variants, 274-

ANTS, 364 275
Anytime planning, 343 Backtracking, 84-85
API, 363, 380, 383, 400 Backward error propagation, 265
Arcs, 27 BACON System, 165-167
ART, 172, 257, 313-322 BASIC, 437
ART-1, 313-322 BB1, 377
ART-2, 313-322 BBH, 200-201
Artificial life, 402-410 BBS, 374
Beam-search, 56
Echo, 403 Behavior architectures, 373-374
Tierra, 403 Behavior-Based Systems (BBS), 374
Simulated evolution, 403-404 Belief-Desire-Intention (BDI)
Variations of artificial life, 408
Lindenmayer systems, 408 architectures, 370-371
Artificial Immune Systems (AIS), 11, Best-First Search (BFS), 50-57
398-402 BestFS, 51-54
Self-management capabilities, 399- Beta parameter, 62
BFS, 39-47, 123
400 Bidirectional Search, 40-41
Artificial Intelligence Markup Bitboard, 110
Blackboard architectures, 369-370
Language (AIML), 361-362 Blank sheets, 4
ART-implementation, 316-322 Bottom-up approach, 7
Asimov, Isaac, 2 Braitenburg vehicles, 334-335
A-star algorithm, 57-63 Breadth-First Search (BFS), 39-47, 123
ATLANTIS, 375-376 Brooks, Rodney, 13, 10, 132
ATN, 9 Bug, 403-408
Atomic sentences, 153
Augmented Transition Network (ATN), 9

Index 489

Building-Block Hypothesis (BBH), Constraint-satisfaction algorithm, 84-86
201-202 Generate and test, 84
Backtracking, 84-85
C Forward checking, 85-86
C language, 437
CA, 393-398 Look ahead, 85-86
CCD, 337 Constraint Satisfaction Problems
Cell decomposition, 343-345
Cellular Automata (CA), 393-398 (CSP), 81-85
Graph coloring, 81-83
One-dimensional CAs, 394-395 Scheduling, 83-84
Two-dimensional CAs, 394-396
Conway application, 396-398 Constraints, 81
Turing completeness, 398 Content-Addressable Memory (CAM),
Emergence and organization, 398
centroid, 305-311 322-323
CG, 358 Conway, John, 11, 395
Charge-Couple-Device (CCD), 337 Cornell Aeronautical Laboratory, 7
ChatterBots, 360 Cost functions, 58-62
Checkers, 2, 4, 15, 90, 106-109 Crossover probability, 230
Checker-board representation, 107 CSP, 81-85
Chess, 2, 4, 15, 25, 90, 95, 109-112, 354 Cube program, 214
Chess-board representation, 110 Cybernetics, 291
Child Machine, 3 Cycle, 27
Chinook, 107-109 D
Chunking, 382 DAI, 353
Classical Game AI, 106-122 DARPA, 332
Checkers, 106-109 Dartmouth AI Conference, 4
Chess, 109-112 Data mining application, 2
Clark, Arthur C., 2 DE algorithm, 228-230
Classes of search, 22 DE, 227-235
Class-node activation, 276 Decision trees, 172-173
Clustering, 296
Colmeraur, Alain, 9 Creating, 174-176
Common sense, 168 Decision-tree learning, 173-176
Communication of knowledge, 167 Declarative knowledge, 144
Compound sentences, 154 Decomposition, 330
Computation knowledge discovery, Deductive reasoning, 151-152
165-167 Deep Blue, 110
Conjunctions, 149 Deep space 1, 12
Constraint relaxation, 81 Deliberative architectures, 368-369
Delta rule, 262
Demons, 147
Dendral Project, 7

490 Artificial Intelligence

Dendrites, 251 EURISKO, 167
Depth-First Search (DFS), 31-32, 93, Evolutionary Computation, 16, 195-244

123 Strategies, 196
Depth-Limited Search (DLS), 33-34 Programming, 197
DFS, 31-32, 93, 123 Genetic algorithms, 197-198
Diagraph, 27-28 Genetic programming, 198
Differential Evolution (DE), 227-235 Biological motivation, 199-200
Genetic algorithms, 200-220
Algorithm, 228-230 Genetic Programming (GP), 212-
Implementation, 230
Discovery spaceship, 2 220
Disjunctions, 149 Evolutionary Strategies (ES), 220-
Disk-square tables, 112
Distributed Artificial Intelligence 227
(DAI), 353 Differential Evolution (DE), 227-
Distributed Problem Solving (DPS), 353
Distributed Sequential Computing 235
(DSC), 381-382 Particle Swarm Optimization
Diversification, 81
DLS, 33-34 (PSO), 236-244
Dorigo, Marco, 11 Evolvable Hardware, 244
DPS, 353 Evolutionary neural networks, 416-422
DSC, 381-382 Genetically evolved neural
DUP instruction, 214
E networks, 416-422
Echo, 403 Evolutionary Strategies (ES), 220-227
Eight Puzzle, 58-65
Demonstration with A*, 64-65 Algorithm, 221-223
Effectors, 338, 351 Implementation, 223-227
Eliza and Parry, 360 Evolutionary strategies algorithm, 221-
Eliza, 6, 9 223
EMACS, 8 Evolvable hardware, 244
Email filtering, 364-365 F
End-game database, 109 Feedforward, 256, 276, 301
End-games, 113 Field-of-View (FOV), 125, 130-131
Engelman, Carl, 7 Fifteen puzzle, 59
Entertainment agents, 358 Fifth Generation Computer Systems
Entropy, 174 Project, 156-157
ES, 220-227 Finite State Machine (FSM), 130-131
Euclidean tour, 70-71 FIPA, 388
First-Order and Prolog, 148, 155
First-Order Logic (Predicate Logic),
152-162
First-person shooter (FPS), 13, 172-
173, 355-356

Index 491

Fitness-proportionate selection, 201 Implementation, 215-220
FOL, 152-163 Genetic recombination, 202
FORTRAN, 437 Go, 114-115
Forward checking, 83-85 Goal generation, 115
Forward propagation, 265 Go-Board representation, 112
Four Color Theorem, 83 GOFAI, 9
FOV, 127 Goldblach’s conjecture, 166
FPS, 13, 172-173, 355-356 Good-old-fashioned-AI, 9
Frames, 146-148 GP, 212-220
Graph algorithms, 32
Generic, 146 Graph coloring, 81-83
Instance, 146 Graphs, 27
Longbowman, 146 Group or distributed robotics, 345
Pikeman, 147 H
FSM, 131-132, 360 HAL, 2-3
Functional programming, 434-435 Hart, Tim, 6
Fuzzy control, 415-416 Haskell, 435
Fuzzy logic, 410-416 Hebb, Donald, 7
Fuzzy logic mapping, 411-414 Hebb’s rule, 291-296
Fuzzy logic operators, 414-415
Fuzzy systems, 410-416 Implementation, 292-296
Fuzzy logic mapping, 411-414 Hebbian learning, 7, 172, 290-291
Fuzzy logic, 410-416 Helper APIs, 31-32
Fuzzy logic operators, 414-415 Hidden-node activation, 277
Fuzzy control, 415-416 Hill-climbing search, 65-66
G History of AI, 1
GA, 199-200, 200-220 Holland, John, 9, 403
Game agents, 358-359 Homer, 376-377
Game of Life, 11, 395-396 Hopfield auto-associator algorithm,
Game theory, 90
General Problem Solver (GPS), 4 323-324
General search paradigms, 31 Hopfield auto-associative model, 322-
General state space search, 22
Generalization, 268, 273 327
Generate and Test method, 31-32, 84 Content-Addressable Memory
Genetic algorithms, 197-199, 200-220, (CAM), 322-323
212-220 Hopfield auto-associator algorithm,
Implementation, 204-211 323-324
Genetic Programming (GP), 198, 212- Hopfield implementation, 324-327
220
Hopfield implementation, 324-327
Horn clauses, 9
HTML, 163-164

492 Artificial Intelligence

HTTP, 350, 353 Karel++, 346
Hybrid agents, 366 Kismet, 331
Hybrid architectures, 371 k-Means, 304
Hybrid models, 17 k-Means algorithm, 305-307
Hyperplane, 258 k-Means clustering, 257, 304-313
I
ID3, 36-37, 173, 176, k-Means algorithm, 305-307
Imperative programming, 437 k-Means implementation, 307-313
Information gathering and filtering, 365 k-Means implementation, 307-313
Information retrieval and KR, 157 Knowledge Query and Manipulation
Informed search, 15, 41-87
Language (KQML), 385-387
Best-First Search (BFS), 50-57 Knowledge Representation (KR), 15,
A*Search, 57-63
Hill-climbing search, 65-66 143-169
Integrated Management Console, 401- Types of knowledge, 144
403 Role of knowledge, 144-145
Intelligent agents, 349-389 Semantic networks, 145-146
Anatomy of an agent, 350-351 Frames, 146-147
Agent properties and AI, 351-353 Propositional logic, 149-152
Agent environments, 353-356 First-Order Logic (Predicate
Agent taxonomies, 356-366 Logic), 152-163
Agent architectures, 366-382 Computation knowledge discovery,
Agent languages, 382-385 165-167
Agent communication, 385-389
Intelligent agents, 17, 132 Ontology, 167
Intensification, 80 Communication of knowledge, 167
Interdisciplinary R&D, 12 Common sense, 168
Interface agents, 356-357
iProlog, 148-149 Kohonen self-organizing maps, 257
Iterative Deepening Search (IDS), Koza, John, 198
36-37 KQML, 385-387
J KQML performatives, 387
Java, 363, 379-380 KR, 143-159
Java Virtual Machine (JVM), 379-380 L
JVM, 379-380 LAMA, 346
K Language taxonomy, 433-442
Karel, 346 Languages of AI, 17
Last-In-First-Out (LIFO), 32
Layered behavior architectures, 130-131
Least-Mean-Squares (LMS) Learning,

250, 257, 262-265
Learning algorithm, 262-263
Implementation, 263-265

Index 493

Lenat, Doug, 168 Mark I, Ferranti, 4
Levin, Mike, 6 Markov models, 177
LIFO queue, 32 Markov chains, 177-181
LIFO stack, 32
Lindenmayer systems, 408-410 Word generation with, 179-180
Linear discriminants, 257-258 Implementation, 180-181
Linear genetic programming, 212 Martin, William, 7
Linear separable problems, 257 Maxims agent, 365
LISP, 6-8, 167, 200, 365, 385-386, 435- McCarthy, John, 6, 9
Mechanical Intelligence, 2
436, 443-451 Messengers, 380-381
History of, 443-444 Messy and Scruffy approaches to AI, 10
Data representation, 444 Meta-knowledge, 144
Simple expressions, 444 Min-conflicts search, 86
Predicates, 445 Minimax algorithm, 92-106
Variables, 445 Implementation for Tic-Tac-Toe,
List processing, 445-446
Conditions, 447-448 98-101
Functions, 448-449 Alpha-beta pruning, 101-106
Minimax search with alpha-beta
LMS, 262-266 pruning, 111
Logic programming, 441-442 Minsky, Marvin, 8, 146
LOGO, 346 MIT, 7
Loki, 120-121 ML, 435
Look ahead, 84-85 MLP, 254-256, 265
Loop, 27 Mobile agents, 362-363
L-systems, 408-410 Mobile architectures, 371-372
Luna 2, 331 Mobility-based evaluation, 112
M Modus Ponens, 149
Machine learning, 16, 172-192 Modus Tollens, 149
Moses, Joel, 7
Machine-learning algorithms, 171- Motion planning, 342-343
172 Movement and path-finding, 121-122
Movement planning, 342-345
Supervised learning, 172-173 MSE, 262
Unsupervised learning, 176-181 MUL instruction, 214
Nearest Neighbor Classification Multi-layer neural network, 273
Multi-layer perceptron (MLP), 250,
(1NN), 185-192 254-256
Machine-learning algorithms, 171-172 Multi-Prob-Cut (MPC), 113
Maclisp, 7 Mutation, 232-233
Macsyma, 7 MYCIN, 8
Management console, 399
Mark I, 259

494 Artificial Intelligence

N N-puzzle, 60, 91
NASA, 12 N-Queens problem, 50-56
Natural Language Processing (NLP), 9 N-Queens problem, 77-78
Natural Language Understanding O
OAA, 377
(NLU), 9 Object avoidance behavior, 341
Nearest Neighbor Classification (1NN), Object-oriented programming (OOP),

185-192 438
Neat and Scruffy approaches to AI, 9 Obliq, 382
Neural network topology, 269 One-dimensional CAs, 392-393
Neural Networks I, 16, 249-285 Ontology, 165
OOP, 438
Short history, 249-250 Open Agent Architectures (OAA), 377
Biological motivation, 250-251 Open dynamics engine, 346
Fundamentals, 251-252 Opening book database, 110-111
Perceptron, 257-261 Opening knowledge, 112
Least-Mean-Squares (LMS) Othello, 112-113
P
Learning, 262-265 Papert, Seymour, 8
Learning with backpropagation, Particle Swarm Optimization (PSO),

265-270 236-244
Probabilistic Neural Networks Algorithm, 236-238
Implementation, 238-244
(PNN), 276-281
Other Neural Network Pattern recognition, 273-274
Pattern-based evaluation, 112
Architectures, 281-283 PDP-6, 6
Neural Networks II, 17, 289-327 Perceptron, 7
Perceptron implementation, 260-261
Unsupervised learning, 289-290 Perceptron learning algorithm, 259
Hebbian learning, 290-291 “Perceptrons” paper, 8, 250
Simple Competitive Learning, 296-313 Perceptron rule, 259
k-Means clustering, 304-305 Perceptron with sensors, 337-338
Adaptive Resonance Theory (ART), Perceptron, 257-261
Phenotypic algorithm, 222
313-322 Planner, 136
Hopfield Auto-associative model, PNN, 276-281
PNN classifier function, 277-279
322-327 Poker, 90, 109, 118-121
Neurogammon, 116-117
Newell, Alan, 4
Nim, 26-27
NLP, 9
NLU, 9
Non-Player-Character (NPC), 124-134
Non-zero-sum game, 90
NPC behavior, 129-130
NPC, 124-134, 352-354, 356-358

Index 495

POP-11, 361, 437, 460-468 RAPT, 346
History of, 460 Ray, Tom, 403
Data representation, 460-462 RBF, 277
Variables, 462 RBS, 136-139
List processing, 462-463 RDF, 164
Conditions, 463-464 Reactive agent, 358
Iteration and maps, 464 Reactive architectures, 367-368
Pattern matching, 465 Reactive control system architecture,
Procedures, 465-468
340
Potential fields, 344-345 Real-time strategy AI, 123, 136
Predicate logic, 152-163 Recombination, 232-233
Principal Variation Search (PVS), 108 Recurrent neural network, 283
Prinz, Dietrich, 4 Remote agent, 12
Probabilistic Neural Networks (PNN), Replacement, 232-233
Resource Description Framework
275-281
Algorithm, 275-277 (RDF), 164
Reversi, 112
Procedural attachment, 147-148 Robot programming languages, 346
Procedural knowledge, 144 Robot simulators, 346
Procedural Reasoning System (PRS), Robotics, 329-346

378-379 Introduction, 329-334
Prodigy, 144 Braitenburg vehicles, 334-335
Prolog, 9, 155-157, 160-161, 468-481 Natural sensing and control, 336-
History of, 469
337
Data representation, 469-470 Perceptron with sensors, 337-338
List processing, 470-471 Actuation with effectors, 338
Facts, rules, and evaluation, 471-480 Robotic control systems, 338-339
Proof-number search, 115 Simple control architectures, 339-
Propositional logic, 149-152
Propositions, 149 342
PRS, 378-379 Movement planning, 342-345
PSO, 236-244 Group or distributed robotics, 345
PSO algorithm, 236-238 Robot programming languages, 346
PSO implementation, 238-244 Robot simulators, 346
Python map function, 435 Robotic control systems, 338-339
Q Rosenblatt, Frank, 7
Quantifiers, 155 Rossum’s Universal Robots, 2
R Roulette wheel selection, 178-179, 201-
Radial-Basis Form (RBF), 277 202, 208-211
Roussel, Phillipe, 9
Ruby, 436

496 Artificial Intelligence

Rule traverse, 161 Tabu search, 75-81
Rule-based programming, 136 Min-conflicts search, 86
Rule-Based Systems (RBS), 136-139 Principal Variation Search (PVS), 108
S Multi-Prob-Cut (MPC), 113
SA, 66-68 Proof-Number Search, 115
Samuel, Arthur, 5, 106 Search functions (common orders), 30
Scheduling as a CSP, 83-84 Search in adversarial game space, 25
Scheme, 435, 451-459 Search in a puzzle space, 23

History of, 452 Towers of Hanoi puzzle, 23-25
Simple expressions, 452-453 Search in physical space, 22
Predicates, 453 Seek power behavior, 343
Variables, 453 Semantic networks, 145-146
Iteration and maps, 456-457 Semantic web, 163-164
Conditions, 455 S-expressions, 198, 200, 212
Procedures, 457-459 Shaft encoder, 339
Scrabble, 120-121 Shannon number, 4
Search, 22-23, 29-45, 50-67, 75-81, 86 Shannon, Claude, 4
Classes of search, 22 Shaw, J.C., 4
General state space search, 22 Shortliffe, 8
Search in physical space, 22 SHRDLU, 6
Search in a puzzle space, 23 Sigmoid function, 270
Search in adversarial game space, 25 Sigmoid squashing function, 267-268
Uninformed search, 29-45 Simbad, 346
Iterative Deepening Search (IDS), Simon, Herbert, 4
36-37 Simple competitive learning, 296-313
Search in adversarial game space, 25
Depth-First Search (DFS), 31-32 Vector quantization, 297-298
Search functions (common orders), 30 Vector quantization
Depth-Limited Search (DLS), 33-34
Iterative Deepening Search (IDS), implementation, 297-305
36-37 Simple control architectures, 339-342
Breadth-First Search (BFS), 38-39 Simulated Annealing (SA), 66-68
Bidirectional search, 40-41 Simulated annealing algorithm, 67-68
Uniform-Cost Search (UCS), 41-42 Simulated annealing demonstration,
Best-First Search (BFS), 50-57
Beam-search, 56 70-75
A*Search, 57-58 Simulated evolution, 403-404
Informed search, 41-87 Single-layer perceptron (SLP), 250,
Hill-climbing Search, 65-66
252-254
Sliding window, 281
SLP, 254-256
Smalltalk, 383
SMTP, 350, 353

Index 497

SOAR, 382 Tesla, Nikola, 330
Solar System, 157-159 Test query, 162
Speech recognition, 281 The Logic Theorist, 4
Stanford University, 7-8 Threshold Logic Unit (TLU), 258
Static board evaluation, 111-112 Tic-Tac-Toe, 90-96
Static evaluation function, 108 Tierra, 403
Static state machine, 129-130 Time-series Processing Architecture,
Strachey, Christopher, 4
Strategic AI, 133-134 282
Strong AI, 5-7, 15, 143 TLU, 258
Subsumption architectures, 131, 372- Touchpoint, 399-401
Touchpoint autonomic managers, 400-
373
Subsumption control system 401
Towers of Hanoi problem, 23-25, 91,
architecture, 340-342
Supervised learning, 16, 172-173, 257 204-211
Supervised learning algorithm, 260 Traveling Salesman Problem (TSP), 68-
Supervised neural network
75, 423-424
algorithms, 16 Trees, 27
Swarm intelligence, 11, 237 Truth table, 149-151
Synaptic junctions, 251 Truth values, 152
Synthesizing emotion, 431-432 TSP, 68-75, 423-429
Synthetic agents, 357-358 Turing, Alan, 3-4
Systems approach, 12-15 Turing complete, 393
T Turing completeness, 398
Table lookup, 124-125 Turing machine, 3
Tabu list, 75 Turing test, 360
Tabu search, 75-81 Two-dimensional CAs, 395-396
Two-player games, 89-91
Variants, 80-87 U
Tabu list, 75 UCS, 41-45
Tabu search algorithm, 77-79 UML, 146
Tabu search algorithm, 77-79 Unified Modeling Language (UML),
Taxonomy of robotics, 332-334
TCL, 384-385 146
TD, 115 Uniform-Cost Search (UCS), 41-42
TD-Gammon, 116-117 Uninformed Search, 15, 29-45
Team AI, 132-133
Telescript, 382-383 Helper APIs, 31-32
Temporal Difference (TD) learning, General search paradigms, 31
115 Depth-First Search (DFS), 31-32
Generate and Test method, 31-32
UnrealEngine, 358

498 Artificial Intelligence

UnrealScript, 358 Viking11, 331
Unsupervised learning, 16, 176-181, Virtual character agents, 357-358
W
257, 289-290 Web spider, 350-351
UseNet, 357 Weiner, Norbert, 291
User assistance agent, 364-366 Weizenbaum, Joseph, 6-7
Werbos, Paul John, 9
Email filtering, 364-365 Widrow-Hoff rule, 262
Information gathering and filtering, Winner-takes-all, 268, 273, 276, 300
Woods, Bill, 9
365 Word-form learning, 177-180
Other user-assistance applications, XML, 389-399
XOR problem, 8
365-366 Zero-address architecture, 213
V Zero-sum game, 89-90
VanMelles, Bill, 8 Zobrist hashing, 109
Variables, 81, 101, 154-155
Variations of artificial life, 408
Vector quantization, 305
Video game AI, 121-139

Movement and path-finding, 123-
124